{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T14:45:27Z","timestamp":1771339527687,"version":"3.50.1"},"reference-count":268,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda??o para a Ci?ncia e a Tecnologia","doi-asserted-by":"publisher","award":["2024.01914.BDANA"],"award-info":[{"award-number":["2024.01914.BDANA"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda??o para a Ci?ncia e a Tecnologia","doi-asserted-by":"publisher","award":["UID\/50008\/2023 IT"],"award-info":[{"award-number":["UID\/50008\/2023 IT"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3636718","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T18:29:37Z","timestamp":1764095377000},"page":"201392-201438","source":"Crossref","is-referenced-by-count":2,"title":["Automating Electrical Grid Asset Inspection: From Current Challenges to Future Directions"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8184-7779","authenticated-orcid":false,"given":"Pedro","family":"Daniel Rocha","sequence":"first","affiliation":[{"name":"Instituto de Telecomunicações, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3641-0186","authenticated-orcid":false,"given":"Fernando J. P.","family":"Lopes","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicações, Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1141-4404","authenticated-orcid":false,"given":"Lu\u00eds A.","family":"Da Silva Cruz","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunicações, Coimbra, Portugal"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"DSOs in the Energy Transition","year":"2024"},{"key":"ref2","volume-title":"Distribution System Operator Observatory 2022: Managing Innovation and RES Grid Connection for a Carbon Neutral Europe","author":"Meletiou","year":"2023"},{"key":"ref3","volume-title":"REPowerEU Plan","author":"Commission","year":"2022"},{"key":"ref4","volume-title":"Electricity Grids and Secure Energy Transitions\u2014Enhancing the Foundations of Resilient, Sustainable and Affordable Power Systems","author":"Agency","year":"2025"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/PESGM.2015.7285949"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s42341-019-00143-5"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.2172\/1891204","article-title":"U. S. solar photovoltaic system and energy storage cost benchmarks, with minimum sustainable price analysis: Q1 2022","author":"Ramasamy","year":"2022"},{"key":"ref8","volume-title":"Global Wind Report 2023","year":"2023"},{"key":"ref9","volume-title":"Electricity Mix","author":"Ritchie","year":"2020"},{"key":"ref10","volume-title":"2024 Hydropower Status Report","author":"Hydropower Assoc","year":"2024"},{"key":"ref11","article-title":"Technology substitution with drones for inspections in the utility sector: Quantitative and qualitative benefits","author":"Rosi","year":"2023"},{"key":"ref12","volume-title":"IEEE Guide for Collecting and Managing Transmission Line Inspection and Maintenance Data, Institute of Electrical and Electronics Engineers","year":"2024"},{"key":"ref13","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.112353","article-title":"Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies","volume":"161","author":"H\u00f8iaas","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.solener.2023.112186","article-title":"A review of automated solar photovoltaic defect detection systems: Approaches, challenges, and future orientations","volume":"266","author":"Hijjawi","year":"2023","journal-title":"Sol. Energy"},{"issue":"11","key":"ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e21491","article-title":"Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach","volume":"9","author":"Sep\u00falveda-Oviedo","year":"2023","journal-title":"Heliyon"},{"key":"ref16","volume-title":"The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World","author":"Domingos","year":"2017"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114683","article-title":"Comparative investigation of imaging techniques, pre-processing and visual fault diagnosis using artificial intelligence models for solar photovoltaic system\u2014A comprehensive review","volume":"232","author":"Balachandran","year":"2024","journal-title":"Measurement"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106445","article-title":"Damage detection techniques for wind turbine blades: A review","volume":"141","author":"Du","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.renene.2020.07.145","article-title":"A review of non-destructive testing on wind turbines blades","volume":"161","author":"Garc\u00eda M\u00e1rquez","year":"2020","journal-title":"Renew. Energy"},{"issue":"4","key":"ref20","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.3390\/applmech3040075","article-title":"A survey on non-destructive smart inspection of wind turbine blades based on Industry 4.0 strategy","volume":"3","author":"Dimitrova","year":"2022","journal-title":"Appl. Mech."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119869"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"33236","DOI":"10.1109\/ACCESS.2024.3371493","article-title":"Review on the advancements in wind turbine blade inspection: Integrating drone and deep learning technologies for enhanced defect detection","volume":"12","author":"Memari","year":"2024","journal-title":"IEEE Access"},{"issue":"6","key":"ref23","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.3390\/app13063564","article-title":"A review of detection technologies for underwater cracks on concrete dam surfaces","volume":"13","author":"Chen","year":"2023","journal-title":"Appl. Sci."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2016.04.011","article-title":"Remote sensing methods for power line corridor surveys","volume":"119","author":"Matikainen","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref25","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","article-title":"Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning","volume":"99","author":"Nguyen","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref26","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.arcontrol.2020.09.002","article-title":"Data analysis in visual power line inspection: An in-depth review of deep learning for component detection and fault diagnosis","volume":"50","author":"Liu","year":"2020","journal-title":"Annu. Rev. Control"},{"key":"ref27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107781","article-title":"Object detection in power line infrastructure: A review of the challenges and solutions","volume":"130","author":"Sharma","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref28","first-page":"1","article-title":"New developments in automated vision-based methods for power line inspection","volume-title":"Proc. 56th North Amer. Power Symp. (NAPS)","author":"Anderson"},{"key":"ref29","volume-title":"Publications Output: U.S. Trends and International Comparisons","author":"Schneider","year":"2023"},{"key":"ref30","volume-title":"Systems Engineering Overview","year":"2025"},{"key":"ref31","volume-title":"What is Enterprise Architecture? A Top Guide to Definition, Process, and Best Practices","year":"2024"},{"key":"ref32","volume-title":"Visual Inspections: A Complete Guide","year":"2025"},{"key":"ref33","volume-title":"NDT (Non-Destructive Testing): A Complete Guide","year":"2025"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1201\/9781003002826"},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-95831-6","volume-title":"Cognitive Biases in Visualizations","author":"Ellis","year":"2018"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2006.01.008"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"33173","DOI":"10.1109\/ACCESS.2024.3369417","article-title":"Opportunities and challenges in data-centric AI","volume":"12","author":"Kumar","year":"2024","journal-title":"IEEE Access"},{"key":"ref38","article-title":"Performance and reliability of photovoltaic systems: Subtask 3.2: Review of failures of photovoltaic modules: IEA PVPS task 13: External final report IEA-PVPS","author":"K\u00f6ntges","year":"2014"},{"key":"ref39","volume-title":"How Do Solar Cells Work?"},{"key":"ref40","first-page":"1219","article-title":"A novel detection method for hot spots of photovoltaic (PV) panels using improved anchors and prediction heads of YOLOv5 network","volume-title":"Energy Rep.","volume":"8","author":"Sun","year":"2022"},{"issue":"16","key":"ref41","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.3390\/en15165966","article-title":"Machine learning in solar plants inspection automation","volume":"15","author":"Starzy\u0144ski","year":"2022","journal-title":"Energies"},{"key":"ref42","volume-title":"Half-Cut Solar Panels","author":"Magazine","year":"2022"},{"key":"ref43","volume-title":"High Voltage Insulators","author":"Holtzhausen","year":"2025"},{"issue":"1","key":"ref44","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3390\/app10010123","article-title":"Analysis of long-term deterioration characteristics of high voltage insulators","volume":"10","author":"Kim","year":"2019","journal-title":"Appl. Sci."},{"key":"ref45","volume-title":"Selection and Dimensioning of High-Voltage Insulators Intended for Use in Polluted Conditions\u2014Part 1: Definitions, Information and General Principles","year":"2008"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1049\/PBPO007E"},{"key":"ref47","article-title":"Insulator defect detection","author":"Lewis","year":"2021","journal-title":"IEEE Dataport"},{"key":"ref48","volume-title":"IEC TS 62446-3:2017 Photovoltaic (PV) Systems\u2014Requirements for Testing, Documentation and Maintenance\u2014Part 3: Photovoltaic Modules and Plants - Outdoor Infrared Thermography.","year":"2017"},{"key":"ref49","volume-title":"Sae J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems","year":"2021"},{"key":"ref50","first-page":"1","article-title":"Beyond AI exposure: Which tasks are cost-effective to automate with computer vision?","volume":"1","author":"Fleming","year":"2025","journal-title":"SSRN Electron. J."},{"key":"ref51","author":"Lewis","year":"2021","journal-title":"Insulator Defect Detection"},{"issue":"6","key":"ref52","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1109\/TPWRD.2020.3046161","article-title":"Intelligent diagnosis of incipient fault in power distribution lines based on corona detection in UV-visible videos","volume":"36","author":"Davari","year":"2021","journal-title":"IEEE Trans. Power Del."},{"key":"ref53","volume-title":"Current Altitude File of The Netherlands Dataroom","year":"2025"},{"key":"ref54","volume-title":"ELPV-Dataset: A Dataset of Functional and Defective Solar Cells Extracted From EL Images of Solar Modules","year":"2018"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3296814","article-title":"Locating the oil leakage on power equipment via ultraviolet-induced hyperspectral imaging technology","volume":"72","author":"Xia","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref56","volume-title":"Portable X Ray Machines\u2014X Ray Library","year":"2025"},{"key":"ref57","volume-title":"Comparison Examples of Cooled and Uncooled Thermal Imaging Cameras"},{"key":"ref58","volume-title":"How Does Emissivity Affect Thermal Imaging?","year":"2021"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2025.3597695"},{"issue":"6","key":"ref60","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.1109\/TDEI.2024.3351093","article-title":"Optical imaging technology application in transmission line insulator monitoring: A review","volume":"31","author":"Liu","year":"2024","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"issue":"10","key":"ref61","doi-asserted-by":"crossref","first-page":"17019","DOI":"10.1109\/JSEN.2024.3385019","article-title":"Corona discharge modes and their detections under DC and AC voltages","volume":"24","author":"Uckol","year":"2024","journal-title":"IEEE Sensors J."},{"key":"ref62","volume-title":"Monitoring Hot Line Washing With Ofil\u2019s Corona Camera","year":"2025"},{"key":"ref63","article-title":"TS40K: A 3D point cloud dataset of rural terrain and electrical transmission system","author":"Lavado","year":"2024","journal-title":"arXiv:2405.13989"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1002\/ese3.781"},{"issue":"12","key":"ref65","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e33208","article-title":"Hyperspectral imaging and its applications: A review","volume":"10","author":"Bhargava","year":"2024","journal-title":"Heliyon"},{"key":"ref66","article-title":"Comparing multispectral and hyperspectral UAV data for detecting peatland vegetation patterns","volume":"132","author":"Pang","year":"2024","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref67","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.112058","article-title":"Evaluation of insulator aging status based on multispectral imaging optimized by hyperspectral analysis","volume":"205","author":"Wang","year":"2022","journal-title":"Measurement"},{"issue":"3","key":"ref68","doi-asserted-by":"crossref","first-page":"4759","DOI":"10.1109\/LRA.2020.3003772","article-title":"LineSpyX: A power line inspection robot based on digital radiography","volume":"5","author":"Gao","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref69","volume-title":"Comparing Approaches to Remote Substation Monitoring\u2014Why a Touchless Approach is Better","year":"2022"},{"key":"ref70","volume-title":"Deciding Between Satellites and Drones for Geospatial Data Collection: A Comparative Analysis","author":"Teodori","year":"2025"},{"key":"ref71","volume-title":"How Drones Have Transformed Energy Inspection","year":"2025"},{"key":"ref72","volume-title":"Vehicle-Mounted Cameras to Inspect Distribution Electrical Grid Assets","year":"2025"},{"key":"ref73","volume-title":"Power Line Inspection \\textbar UAV Asset Inspection \\textbar QLD","author":"Helicopters","year":"2025"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPETS.2015.2395388","article-title":"LineScout technology opens the way to robotic inspection and maintenance of high-voltage power lines","volume":"2","author":"Pouliot","year":"2015","journal-title":"IEEE Power Energy Technol. Syst. J."},{"key":"ref75","volume-title":"HX-850 Industrial Drone Frame","year":"2025"},{"key":"ref76","volume-title":"FLY2100 Cost-Effective Fixed-Wing Drone","year":"2025"},{"issue":"1","key":"ref77","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3390\/aerospace10010058","article-title":"Simulation framework for real-time PHM applications in a system-of-systems environment","volume":"10","author":"Dingeldein","year":"2023","journal-title":"Aerospace"},{"issue":"1","key":"ref78","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TPWRD.2021.3059307","article-title":"Automated power lines vegetation monitoring using high-resolution satellite imagery","volume":"37","author":"Gazzea","year":"2022","journal-title":"IEEE Trans. Power Del."},{"key":"ref79","volume-title":"Assessing the Value and Efficiency of Virtual Inspections","year":"2023"},{"key":"ref80","first-page":"5927","article-title":"Mechanism design for new sensors field deployment by LineRanger powerline robot","volume-title":"Proc. IEEE Int. Conf. Robot. Autom. (ICRA)","author":"Richard"},{"key":"ref81","first-page":"86","article-title":"A lightweight device for energy harvesting from power lines with a fixed-wing UAV","volume-title":"Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS)","author":"Stewart"},{"key":"ref82","first-page":"13551","article-title":"Autonomous overhead powerline recharging for uninterrupted drone operations","volume-title":"Proc. IEEE Int. Conf. Robot. Autom. (ICRA)","author":"Hoang"},{"key":"ref83","first-page":"973","article-title":"Development of a power line inspection robot with hybrid operation modes","volume-title":"Proc. IEEE\/RSJ Int. Conf. Intell. Robots Syst. (IROS)","author":"Chang"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/s13349-024-00820-x"},{"key":"ref85","first-page":"1","article-title":"An ultrasmall bolt defect detection method for transmission line inspection","volume":"72","author":"Luo","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fenrg.2022.990509","article-title":"A rapid warning method for wildfire occurrences along the transmission corridor driven by power system monitoring data","volume":"10","author":"Duan","year":"2022","journal-title":"Frontiers Energy Res."},{"issue":"7553","key":"ref87","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1103\/revmodphys.34.123"},{"issue":"6088","key":"ref89","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"issue":"11","key":"ref90","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref91","first-page":"1137","article-title":"Efficient learning of sparse representations with an energy-based model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ranzato"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553486"},{"key":"ref93","first-page":"84","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"60","author":"Krizhevsky"},{"key":"ref94","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref95","first-page":"1","article-title":"Going deeper with convolutions","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Szegedy"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref99","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref100","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"arXiv:1905.11946"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref102","first-page":"200","article-title":"An overview of bag of words;importance, implementation, applications, and challenges","volume-title":"Proc. Int. Eng. Conf. (IEC)","author":"Qader"},{"key":"ref103","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"arXiv:1301.3781"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"issue":"8","key":"ref105","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref106","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"arXiv:1706.03762"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref108","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv:1810.04805"},{"key":"ref109","article-title":"An image is worth 16\\times16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref110","article-title":"Training data-efficient image transformers & distillation through attention","author":"Touvron","year":"2020","journal-title":"arXiv:2012.12877"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3506283"},{"key":"ref113","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021","journal-title":"arXiv:2103.00020"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.12092"},{"key":"ref115","first-page":"23716","article-title":"Flamingo: A visual language model for few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alayrac"},{"key":"ref116","first-page":"1","article-title":"PaLI: A jointly-scaled multilingual language-image model","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref118","article-title":"Qwen-VL: A versatile vision-language model for understanding, localization, text reading, and beyond","author":"Bai","year":"2023","journal-title":"arXiv:2308.12966"},{"key":"ref119","volume-title":"GPT-4V(ision) Technical Work and Authors","year":"2025"},{"key":"ref120","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref121","article-title":"PaliGemma 2: A family of versatile VLMs for transfer","author":"Steiner","year":"2024","journal-title":"arXiv:2412.03555"},{"key":"ref122","article-title":"Florence-VL: Enhancing vision-language models with generative vision encoder and depth-breadth fusion","author":"Chen","year":"2024","journal-title":"arXiv:2412.04424"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref125","article-title":"Supervised contrastive learning","author":"Khosla","year":"2020","journal-title":"arXiv:2004.11362"},{"key":"ref126","volume-title":"Contrastive Representation Learning","author":"Weng","year":"2021"},{"key":"ref127","article-title":"Time travel in LLMs: Tracing data contamination in large language models","author":"Golchin","year":"2023","journal-title":"arXiv:2308.08493"},{"key":"ref128","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"Finn","year":"2017","journal-title":"arXiv:1703.03400"},{"key":"ref129","article-title":"Gradient-based hyperparameter optimization through reversible learning","author":"Maclaurin","year":"2015","journal-title":"arXiv:1502.03492"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1611.01578"},{"key":"ref131","article-title":"On the power of curriculum learning in training deep networks","author":"Hacohen","year":"2019","journal-title":"arXiv:1904.03626"},{"key":"ref132","article-title":"Frontier models are capable of in-context scheming","author":"Meinke","year":"2024","journal-title":"arXiv:2412.04984"},{"key":"ref133","volume-title":"Data-Centric AI","author":"Ng","year":"2025"},{"key":"ref134","volume-title":"Homepage","author":"Ng","year":"2025"},{"key":"ref135","article-title":"Deep learning is robust to massive label noise","author":"Rolnick","year":"2017","journal-title":"arXiv:1705.10694"},{"key":"ref136","author":"Spectrum","year":"2025","journal-title":"Andrew Ng: Unbiggen AI"},{"key":"ref137","volume-title":"Finding Millions of Label Errors With Cleanlab","author":"Curtis","year":"2022"},{"key":"ref138","volume-title":"Open-source Repository","year":"2025"},{"key":"ref139","first-page":"1","article-title":"ActiveLab: Active learning with re-labeling by multiple annotators","volume-title":"Proc. ICLR Workshop Trustworthy ML","author":"Goh"},{"key":"ref140","volume-title":"Andrew Ng Explores The Rise Of AI Agents and Agentic Reasoning\u2014BUILD 2024 Keynote","year":"2024"},{"key":"ref141","article-title":"CRITIC: Large language models can self-correct with tool-interactive critiquing","author":"Gou","year":"2023","journal-title":"arXiv:2305.11738"},{"key":"ref142","article-title":"Efficient tool use with chain-of-abstraction reasoning","author":"Ao","year":"2024","journal-title":"arXiv:2401.17464"},{"key":"ref143","article-title":"HuggingGPT: Solving AI tasks with ChatGPT and its friends in hugging face","author":"Shen","year":"2023","journal-title":"arXiv:2303.17580"},{"key":"ref144","article-title":"AutoGen: Enabling next-gen LLM applications via multi-agent conversation","author":"Wu","year":"2023","journal-title":"arXiv:2308.08155"},{"key":"ref145","article-title":"MetaGPT: Meta programming for a multi-agent collaborative framework","author":"Hong","year":"2024","journal-title":"arXiv:2308.00352"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01212-6"},{"issue":"12","key":"ref147","doi-asserted-by":"crossref","first-page":"4617","DOI":"10.3390\/s22124617","article-title":"Lightweight hot-spot fault detection model of photovoltaic panels in UAV remote-sensing image","volume":"22","author":"Zheng","year":"2022","journal-title":"Sensors"},{"key":"ref148","first-page":"1","article-title":"Infrared solar module dataset for anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent","author":"Millendorf"},{"key":"ref149","article-title":"Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network","volume":"48","author":"Le","year":"2021","journal-title":"Sustain. Energy Technol. Assessments"},{"key":"ref150","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113135","article-title":"Thermal inspection of photovoltaic modules with deep convolutional neural networks on edge devices in AUV","volume":"218","author":"Le","year":"2023","journal-title":"Measurement"},{"key":"ref151","volume-title":"PV-Hawk: Tool for the Extraction and Mapping of Photovoltaics Modules From IR Drone Videos of Utility-Scale PV Plants","author":"Bommes","year":"2022"},{"key":"ref152","volume-title":"PV-Hawk: Tool for the Extraction and Mapping of Photovoltaics Modules From IR Drone Videos of Utility-Scale PV Plants","author":"Bommes","year":"2022"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1002\/pip.3448"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.1002\/pip.3564"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.1002\/pip.3518"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2021.1948372"},{"issue":"3","key":"ref157","doi-asserted-by":"crossref","first-page":"1244","DOI":"10.3390\/s22031244","article-title":"Region-based CNN for anomaly detection in PV power plants using aerial imagery","volume":"22","author":"Vlaminck","year":"2022","journal-title":"Sensors"},{"key":"ref158","volume-title":"YOLOv5 Architecture Summary","year":"2023"},{"key":"ref159","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.solener.2019.02.067","article-title":"Automatic classification of defective photovoltaic module cells in electroluminescence images","volume":"185","author":"Deitsch","year":"2019","journal-title":"Sol. Energy"},{"key":"ref160","volume-title":"Photovoltaic Cell Anomaly Detection Dataset","author":"Su","year":"2022"},{"issue":"1","key":"ref161","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/TII.2022.3162846","article-title":"PVEL-AD: A large-scale open-world dataset for photovoltaic cell anomaly detection","volume":"19","author":"Su","year":"2023","journal-title":"IEEE Trans. Ind. Informat."},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1080\/15567036.2023.2230935"},{"key":"ref163","volume-title":"UCF-EL-Defect Dataset","author":"Fioresi","year":"2022"},{"key":"ref164","article-title":"Advancing automatic photovoltaic defect detection using semi-supervised semantic segmentation of electroluminescence images","author":"Jha","year":"2024","journal-title":"arXiv:2404.13693"},{"key":"ref165","volume-title":"IEC TS 60904-13:2018 Photovoltaic Devices\u2014Part 13: Electroluminescence of Photovoltaic Modules","year":"2018"},{"key":"ref166","volume-title":"DTU\u2014Drone Inspection Images of Wind Turbine","author":"Shihavuddin","year":"2018"},{"key":"ref167","volume-title":"DTU-Wind Turbine Blade Drone Inspection Images","author":"Gohar","year":"2025"},{"issue":"4","key":"ref168","doi-asserted-by":"crossref","first-page":"676","DOI":"10.3390\/en12040676","article-title":"Wind turbine surface damage detection by deep learning aided drone inspection analysis","volume":"12","author":"Shihavuddin","year":"2019","journal-title":"Energies"},{"key":"ref169","first-page":"290","article-title":"Automatic defect detection in wind turbine blade images: Model benchmarks and re-annotations","volume-title":"Proc. IEEE Int. Conf. Multimedia Expo. Workshops (ICMEW)","author":"Gohar"},{"key":"ref170","volume-title":"Blade30: A Comprehensive Dataset for Multiple Blade-Related Tasks","author":"Yang","year":"2025"},{"key":"ref171","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109615","article-title":"Enhanced defect detection on wind turbine blades using binary segmentation masks and YOLO","volume":"120","author":"Rizvi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref172","first-page":"1","article-title":"Wind turbine actual defects detection based on visible and infrared image fusion","volume":"72","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref173","first-page":"1287","article-title":"A benchmark for visual identification of defective solar cells in electroluminescence imagery","volume-title":"Proc. 35th Eur. Photovoltaic Sol. Energy Conf. Exhib.","author":"Brabec"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-021-01191-9"},{"key":"ref175","volume-title":"Labelimg: The Popular Image Annotation Tool","year":"2025"},{"issue":"1","key":"ref176","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/JPHOTOV.2021.3131059","article-title":"Automated defect detection and localization in photovoltaic cells using semantic segmentation of electroluminescence images","volume":"12","author":"Fioresi","year":"2022","journal-title":"IEEE J. Photovolt."},{"key":"ref177","article-title":"A white paper on wind turbine blade defect and damage categorization: Current state of the industry","author":"Malkin","year":"2020"},{"key":"ref178","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.renene.2022.12.063","article-title":"Towards accurate image stitching for drone-based wind turbine blade inspection","volume":"203","author":"Yang","year":"2023","journal-title":"Renew. Energy"},{"key":"ref179","volume-title":"Bridge Crack Detection","author":"Xu","year":"2025"},{"key":"ref180","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.110641","article-title":"A deep residual neural network framework with transfer learning for concrete dams patch-level crack classification and weakly-supervised localization","volume":"188","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref181","volume-title":"Road Damage Detector","author":"Arya","year":"2025"},{"key":"ref182","article-title":"Underwater crack pixel-wise identification and quantification for dams via lightweight semantic segmentation and transfer learning","volume":"144","author":"Li","year":"2022","journal-title":"Autom. Construction"},{"key":"ref183","volume-title":"DSI: Dam Surface Inspection Dataset","author":"Hong","year":"2025"},{"issue":"2","key":"ref184","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3390\/buildings13020285","article-title":"Multiple defects inspection of dam spillway surface using deep learning and 3D reconstruction techniques","volume":"13","author":"Hong","year":"2023","journal-title":"Buildings"},{"key":"ref185","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105430","article-title":"Underwater dam crack image generation based on unsupervised image-to-image translation","volume":"163","author":"Huang","year":"2024","journal-title":"Autom. Construct."},{"key":"ref186","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105727","article-title":"Enhancement of underwater dam crack images using multi-feature fusion","volume":"167","author":"Chen","year":"2024","journal-title":"Autom. Construct."},{"key":"ref187","first-page":"3708","article-title":"Road crack detection using deep convolutional neural network","volume-title":"Proc. IEEE Int. Conf. Image Process. (ICIP)","author":"Zhang"},{"key":"ref188","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12564"},{"issue":"14","key":"ref189","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.3390\/app9142867","article-title":"Automatic bridge crack detection using a convolutional neural network","volume":"9","author":"Xu","year":"2019","journal-title":"Appl. Sci."},{"key":"ref190","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-022-01327-5"},{"key":"ref191","volume-title":"SCENE-Net-V2: Interpretable Multiclass 3D Scene Understanding With Geometric Priors","author":"Lavado","year":"2025"},{"key":"ref192","first-page":"1","article-title":"SCENE-Net V2: Interpretable multiclass 3D scene understanding with geometric priors","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML) Workshop Geometry-Grounded Represent. Learn. Generative Model.","author":"Lavado"},{"key":"ref193","article-title":"Point transformer v3: Simpler, faster, stronger","author":"Wu","year":"2023","journal-title":"arXiv:2312.10035"},{"key":"ref194","first-page":"1538","article-title":"PSFE-Net: Semantic segmentation network for airborne LiDAR transmission corridor scenes inspection","volume-title":"Proc. 9th Asia Conf. Power Elect. Eng. (ACPEE)","author":"Dong"},{"key":"ref195","first-page":"14499","article-title":"SCF-Net: Learning spatial contextual features for large-scale point cloud segmentation","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Fan"},{"key":"ref196","doi-asserted-by":"crossref","first-page":"97495","DOI":"10.1109\/ACCESS.2021.3094127","article-title":"DALES objects: A large scale benchmark dataset for instance segmentation in aerial LiDAR","volume":"9","author":"Singer","year":"2021","journal-title":"IEEE Access"},{"key":"ref197","first-page":"478","article-title":"Real-time LiDAR-based semantic classification for powerline inspection","volume-title":"Proc. Int. Conf. Unmanned Aircr. Syst. (ICUAS)","author":"Valseca"},{"key":"ref198","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.02413"},{"issue":"2","key":"ref199","doi-asserted-by":"crossref","first-page":"11754","DOI":"10.1016\/j.ifacol.2023.10.562","article-title":"Autonomous point cloud segmentation for power lines inspection in smart grid","volume":"56","author":"Kyuroson","year":"2023","journal-title":"IFAC-PapersOnLine"},{"issue":"11","key":"ref200","doi-asserted-by":"crossref","first-page":"392","DOI":"10.3390\/ijgi13110392","article-title":"Power corridor safety hazard detection based on airborne 3D laser scanning technology","volume":"13","author":"Wang","year":"2024","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref201","article-title":"Low-resource white-box semantic segmentation of supporting towers on 3D point clouds via signature shape identification","author":"Lavado","year":"2023","journal-title":"arXiv:2306.07809"},{"issue":"9","key":"ref202","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1038\/s42256-019-0087-3","article-title":"Towards a topological\u2013geometrical theory of group equivariant non-expansive operators for data analysis and machine learning","volume":"1","author":"Bergomi","year":"2019","journal-title":"Nature Mach. Intell."},{"key":"ref203","volume-title":"LiPowerline LiDAR Point Cloud Processing Power Line Inspection Software\u2014GreenValley International","author":"International","year":"2025"},{"key":"ref204","volume-title":"VEPL","author":"Cano-Solis","year":"2023"},{"issue":"8","key":"ref205","doi-asserted-by":"crossref","first-page":"128","DOI":"10.3390\/data8080128","article-title":"VEPL dataset: A vegetation encroachment in power line corridors dataset for semantic segmentation of drone aerial orthomosaics","volume":"8","author":"Cano-Solis","year":"2023","journal-title":"Data"},{"key":"ref206","volume-title":"RailFOD23 Code","author":"Chen","year":"2025"},{"key":"ref207","volume-title":"RailFOD23 Dataset","author":"Chen","year":"2023"},{"issue":"1","key":"ref208","article-title":"RailFOD23: A dataset for foreign object detection on railroad transmission lines","volume":"11","author":"Chen","year":"2024","journal-title":"Sci. Data"},{"key":"ref209","doi-asserted-by":"crossref","first-page":"172325","DOI":"10.1109\/ACCESS.2024.3484156","article-title":"Foreign object obstruction evaluation for distribution network inspection","volume":"12","author":"Sumagayan","year":"2024","journal-title":"IEEE Access"},{"issue":"11","key":"ref210","doi-asserted-by":"crossref","first-page":"454","DOI":"10.3390\/ijgi12110454","article-title":"VEPL-Net: A deep learning ensemble for automatic segmentation of vegetation encroachment in power line corridors using UAV imagery","volume":"12","author":"Cano-Solis","year":"2023","journal-title":"ISPRS Int. J. Geo-Inf."},{"issue":"6","key":"ref211","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1109\/TPWRD.2020.3043433","article-title":"Intelligent detection of vegetation encroachment of power lines with advanced stereovision","volume":"36","author":"Rong","year":"2021","journal-title":"IEEE Trans. Power Del."},{"issue":"8","key":"ref212","doi-asserted-by":"crossref","first-page":"361","DOI":"10.3390\/drones8080361","article-title":"Foreign object detection network for transmission lines from unmanned aerial vehicle images","volume":"8","author":"Wang","year":"2024","journal-title":"Drones"},{"issue":"5","key":"ref213","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1109\/TPWRD.2023.3279891","article-title":"Detection of foreign objects intrusion into transmission lines using diverse generation model","volume":"38","author":"Wu","year":"2023","journal-title":"IEEE Trans. Power Del."},{"issue":"8","key":"ref214","doi-asserted-by":"crossref","first-page":"346","DOI":"10.3390\/drones8080346","article-title":"Power transmission lines foreign object intrusion detection method for drone aerial images based on improved YOLOv8 network","volume":"8","author":"Sun","year":"2024","journal-title":"Drones"},{"issue":"23","key":"ref215","doi-asserted-by":"crossref","first-page":"12775","DOI":"10.3390\/app132312775","article-title":"Foreign-object detection in high-voltage transmission line based on improved YOLOv8m","volume":"13","author":"Wang","year":"2023","journal-title":"Appl. Sci."},{"key":"ref216","first-page":"1","article-title":"FusionNet: Detection of foreign objects in transmission lines during inclement weather","volume":"73","author":"Ji","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref217","volume-title":"EPRI10: Power Delivery Inspection Imagery Data Sets","year":"2025"},{"key":"ref218","author":"Lewis","year":"2025","journal-title":"IEEE Dataport\u2014Epri Datasets"},{"key":"ref219","author":"Lewis","year":"2022","journal-title":"EPRI Electric Transmission Imagery"},{"key":"ref220","first-page":"820","article-title":"Object classification with YOLOv5 for electric utility asset inspection using UAVs","volume-title":"Proc. Int. Conf. Artif. Intell. Inf. Commun. (ICAIIC)","author":"Yeh"},{"key":"ref221","author":"Lewis","year":"2021","journal-title":"EPRI Distribution Inspection Imagery"},{"key":"ref222","first-page":"347","article-title":"Towards automated visual inspection of electrical grid assets for the smart grid\u2014An application to HV insulators","volume-title":"Proc. IEEE Int. Conf. Commun., Control, Comput. Technol. Smart Grids (SmartGridComm)","author":"Lopes"},{"key":"ref223","volume-title":"Visualization Tools for Insulator-Defect Detection Dataset","author":"Zheng","year":"2024"},{"issue":"22","key":"ref224","doi-asserted-by":"crossref","first-page":"8801","DOI":"10.3390\/s22228801","article-title":"Insulator-defect detection algorithm based on improved YOLOv7","volume":"22","author":"Zheng","year":"2022","journal-title":"Sensors"},{"key":"ref225","article-title":"CPLID: Chinese power line insulator dataset","author":"Tao","year":"2018"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109259"},{"key":"ref227","article-title":"Synthetic HV PL insulator dataset","author":"Ferraz","year":"2024"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.1007\/s41315-024-00349-8"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-024-02691-3"},{"key":"ref230","article-title":"CID: Catenary insulator defect dataset","author":"Zhang","year":"2024"},{"key":"ref231","article-title":"FastFlow: Unsupervised anomaly detection and localization via 2D normalizing flows","author":"Yu","year":"2021","journal-title":"arXiv:2111.07677"},{"key":"ref232","first-page":"1","article-title":"Catenary insulator defect detection: A dataset and an unsupervised baseline","volume":"73","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref233","first-page":"15979","article-title":"Masked autoencoders are scalable vision learners","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"He"},{"key":"ref234","volume-title":"Su22kv Broken Dataset","year":"2022"},{"key":"ref235","doi-asserted-by":"crossref","DOI":"10.3389\/fnbot.2023.1331427","article-title":"ID-YOLOv7: An efficient method for insulator defect detection in power distribution network","volume":"17","author":"Chen","year":"2024","journal-title":"Frontiers Neurorobotics"},{"key":"ref236","volume-title":"VPMBGI: Vietnamese Public Merged Dataset of Broken Glass Insulator for UAV Inspection of Power Lines","author":"Benelmostafa","year":"2023"},{"key":"ref237","volume-title":"Google Colab Notebook for Training YOLOv8 Gold Model on VPMBGI Dataset","author":"Benelmostafa","year":"2025"},{"key":"ref238","first-page":"309","article-title":"Detecting broken glass insulators for automated UAV power line inspection based on an improved YOLOv8 model","volume-title":"Proc. Int. Conf. Adv. Intell. Syst. Sustain. Develop. (AI2SD)","author":"Benelmostafa"},{"issue":"10","key":"ref239","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.3390\/math12101507","article-title":"Optimizing insulator defect detection with improved DETR models","volume":"12","author":"Li","year":"2024","journal-title":"Math."},{"key":"ref240","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0307684"},{"issue":"4","key":"ref241","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","article-title":"Detection of power line insulator defects using aerial images analyzed with convolutional neural networks","volume":"50","author":"Tao","year":"2020","journal-title":"IEEE Trans. Syst., Man, Cybern., Syst."},{"key":"ref242","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2023.108982","article-title":"Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV","volume":"148","author":"Souza","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref243","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2024.110688","article-title":"A synthetic high-voltage power line insulator images dataset","volume":"55","author":"Bianchi","year":"2024","journal-title":"Data Brief"},{"key":"ref244","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2024.110688","volume-title":"Synthetic High-Voltage Power Line Insulator Images","author":"Bianchi","year":"2024"},{"key":"ref245","volume-title":"Inspection of Power Line Assets Dataset (insplad)","author":"Vieira","year":"2023"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2023.2283900"},{"key":"ref247","first-page":"8231","article-title":"Attention modules improve image-level anomaly detection for industrial inspection: A DifferNet case study","volume-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis. (WACV)","author":"Vieira e Silva"},{"key":"ref248","volume-title":"RD-YOLO: Towards Power Components Identification and Rust Defect Detection for Future Unmanned Transmission Lines Maintenance","author":"Liu","year":"2024"},{"key":"ref249","first-page":"1","article-title":"Corrosion detection method of transmission line components in mining area based on multiscale enhanced fusion","volume":"2022","author":"Wu","year":"2022","journal-title":"Mobile Inf. Syst."},{"key":"ref250","doi-asserted-by":"crossref","first-page":"146281","DOI":"10.1109\/ACCESS.2021.3123158","article-title":"Aerial image analysis using deep learning for electrical overhead line network asset management","volume":"9","author":"Odo","year":"2021","journal-title":"IEEE Access"},{"key":"ref251","volume-title":"Cable Damage Dataset","year":"2023"},{"issue":"4","key":"ref252","doi-asserted-by":"crossref","first-page":"739","DOI":"10.3390\/electronics13040739","article-title":"Overhead power line damage detection: An innovative approach using enhanced YOLOv8","volume":"13","author":"Wu","year":"2024","journal-title":"Electronics"},{"key":"ref253","first-page":"1","article-title":"A smart thermography camera and application in the diagnosis of electrical equipment","volume":"70","author":"Han","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"ref254","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/TPWRD.2022.3191694","article-title":"Infrared image target detection of substation electrical equipment using an improved faster R-CNN","volume":"38","author":"Ou","year":"2023","journal-title":"IEEE Trans. Power Del."},{"issue":"8","key":"ref255","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/TIM.2020.2965635","article-title":"Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref256","first-page":"1","article-title":"A box-supervised instance segmentation method for insulator infrared images based on shuffle polarized self-attention","volume":"72","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref257","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2024.3385019"},{"key":"ref258","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3396833","article-title":"DC corona discharge mode identification based on the visible light images via the YOLOv8","volume":"73","author":"Ibrahim Uckol","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"ref259","doi-asserted-by":"crossref","first-page":"2766","DOI":"10.1109\/TPWRD.2021.3116489","article-title":"Corona detection and power equipment classification based on GoogleNet-AlexNet: An accurate and intelligent defect detection model based on deep learning for power distribution lines","volume":"37","author":"Davari","year":"2022","journal-title":"IEEE Trans. Power Del."},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.3390\/data8070118"},{"key":"ref261","volume-title":"DeepLabv3Plus-Pytorch Training With a Custom Dataset Loader","author":"Gomes","year":"2023"},{"issue":"7","key":"ref262","doi-asserted-by":"crossref","first-page":"118","DOI":"10.3390\/data8070118","article-title":"A semantically annotated 15-class ground truth dataset for substation equipment to train semantic segmentation models","volume":"8","author":"Gomes","year":"2023","journal-title":"Data"},{"key":"ref263","volume-title":"DL\/T 664-2016: Application Rules of Infrared Diagnosis for Live Electrical Equipment","year":"2016"},{"key":"ref264","volume-title":"Visualization Tools for Substation Equipment Dataset","author":"Ninja","year":"2024"},{"key":"ref265","volume-title":"A YOLO Annotated 15-Class Ground Truth Dataset for Substation Equipment","author":"Gomes","year":"2023"},{"issue":"1","key":"ref266","doi-asserted-by":"crossref","first-page":"7283","DOI":"10.1038\/s41467-023-42992-y","article-title":"Exploiting redundancy in large materials datasets for efficient machine learning with less data","volume":"14","author":"Li","year":"2023","journal-title":"Nature Commun."},{"key":"ref267","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA66905.2025.11259463"},{"key":"ref268","first-page":"1","article-title":"Advances in electrical grid assets inspection: Exploring multimodal large language models","volume-title":"Proc. 25th Int. Conf. Digit. Signal Process. (DSP)","author":"Rocha"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11267386.pdf?arnumber=11267386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T06:20:13Z","timestamp":1764915613000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11267386\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":268,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3636718","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}