{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:14:14Z","timestamp":1774066454753,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union and Greek national funds","award":["T2EDK-03595"],"award-info":[{"award-number":["T2EDK-03595"]}]},{"name":"European Union and Greek national funds","award":["AdVISEr"],"award-info":[{"award-number":["AdVISEr"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power line inspection is one important task performed by electricity distribution network operators worldwide. It is part of the equipment maintenance for such companies and forms a crucial procedure since it can provide diagnostics and prognostics about the condition of the power line network. Furthermore, it helps with effective decision making in the case of fault detection. Nowadays, the inspection of power lines is performed either using human operators that scan the network on foot and search for obvious faults, or using unmanned aerial vehicles (UAVs) and\/or helicopters equipped with camera sensors capable of recording videos of the power line network equipment, which are then inspected by human operators offline. In this study, we propose an autonomous, intelligent inspection system for power lines, which is equipped with camera sensors operating in the visual (Red\u2013Green\u2013Blue (RGB) imaging) and infrared (thermal imaging) spectrums, capable of providing real-time alerts about the condition of power lines. The very first step in power line monitoring is identifying and segmenting them from the background, which constitutes the principal goal of the presented study. The identification of power lines is accomplished through an innovative hybrid approach that combines RGB and thermal data-processing methods under a custom-made drone platform, providing an automated tool for in situ analyses not only in offline mode. In this direction, the human operator role is limited to the flight-planning and control operations of the UAV. The benefits of using such an intelligent UAV system are many, mostly related to the timely and accurate detection of possible faults, along with the side benefits of personnel safety and reduced operational costs.<\/jats:p>","DOI":"10.3390\/s23208441","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T10:16:12Z","timestamp":1697192172000},"page":"8441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A UAV Intelligent System for Greek Power Lines Monitoring"],"prefix":"10.3390","volume":"23","author":[{"given":"Aikaterini","family":"Tsellou","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering (ECE), Technical University of Crete, 73100 Chania, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1255-461X","authenticated-orcid":false,"given":"George","family":"Livanos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering (ECE), Technical University of Crete, 73100 Chania, Greece"}]},{"given":"Dimitris","family":"Ramnalis","sequence":"additional","affiliation":[{"name":"GeoSense, 57013 Thessaloniki, Greece"}]},{"given":"Vassilis","family":"Polychronos","sequence":"additional","affiliation":[{"name":"GeoSense, 57013 Thessaloniki, Greece"}]},{"given":"Georgios","family":"Plokamakis","sequence":"additional","affiliation":[{"name":"Hellenic Electricity Distribution Network Operator S.A., 11743 Athens, Greece"}]},{"given":"Michalis","family":"Zervakis","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering (ECE), Technical University of Crete, 73100 Chania, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4826-6388","authenticated-orcid":false,"given":"Konstantia","family":"Moirogiorgou","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering (ECE), Technical University of Crete, 73100 Chania, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713634","DOI":"10.3389\/fenrg.2021.713634","article-title":"Unmanned Aerial Vehicle for Transmission Line Inspection: Status, Standardization, and Perspectives","volume":"9","author":"Li","year":"2021","journal-title":"Front. Energy Res."},{"key":"ref_2","unstructured":"(2022, February 14). Drone Industry Insights. Available online: https:\/\/droneii.com\/product\/drones-in-energy-industry-report."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"130410","DOI":"10.1109\/ACCESS.2021.3110159","article-title":"An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: A comprehensive review","volume":"9","author":"Foudeh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Zhu, L. (2023). A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones, 7.","DOI":"10.3390\/drones7060398"},{"key":"ref_5","first-page":"109","article-title":"Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends","volume":"16","author":"Mohsan","year":"2023","journal-title":"Intel. Serv. Robotics"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tsellou, A., Moirogiorgou, K., Plokamakis, G., Livanos, G., Kalaitzakis, K., and Zervakis, M. (2022, January 21\u201323). Aerial video inspection of Greek power lines structures using machine learning techniques. Proceedings of the 2022 IEEE International Conference on Imaging Systems and Techniques (IST), Virtual.","DOI":"10.1109\/IST55454.2022.9827761"},{"key":"ref_7","unstructured":"Yetgin, \u00d6.E., and GEREK, \u00d6.N. (2019). Powerline Image Dataset (Infrared-IR and Visible Light-VL), Version 8, Elsevier. Mendeley Data."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Solilo, M., Doorsamy, W., and Paul, B.S. (2021, January 9\u201310). UAV Power Line Detection and Tracking using a Color Transformation. Proceedings of the 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa.","DOI":"10.1109\/ICECET52533.2021.9698499"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104609","DOI":"10.1016\/j.micpro.2022.104609","article-title":"Autonomous Power Line Detection and Tracking System Using UAVs","volume":"94","author":"Schofield","year":"2022","journal-title":"Microprocess. Microsyst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106987","DOI":"10.1016\/j.ijepes.2021.106987","article-title":"UAV-Lidar Aids Automatic Intelligent Powerline Inspection","volume":"130","author":"Guan","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s10846-022-01725-x","article-title":"Visual-based Assistive Method for UAV Power Line Inspection and Landing","volume":"106","author":"Diniz","year":"2022","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gubbi, J., Varghese, A., and Balamuralidhar, P. (2017, January 8\u201312). A New Deep Learning Architecture for Detection of Long Linear Infrastructure. Proceedings of the 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), Nagoya, Japan.","DOI":"10.23919\/MVA.2017.7986837"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yang, W., Yu, H., Zhang, H., and Xia, G.S. (2019). Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints. Remote Sens., 11.","DOI":"10.3390\/rs11111342"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s00138-020-01138-6","article-title":"LS-Net: Fast Single-Shot Line-Segment Detector","volume":"32","author":"Nguyen","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8196","DOI":"10.1109\/JSEN.2022.3157336","article-title":"Vision-Based Power Line Segmentation with an Attention Fusion Network","volume":"22","author":"Yang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, G., Zhang, M., Li, Q., Liu, X., Li, T., Zhao, L., Liu, K., and Qin, L. (2022). A Lightweight Aerial Power Line Segmentation Algorithm Based on Attention Mechanism. Machines, 10.","DOI":"10.3390\/machines10100881"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jaffari, R., Hashmani, M.A., and Reyes-Aldasoro, C.C. (2021). A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net. Sensors, 21.","DOI":"10.3390\/s21082803"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12220","DOI":"10.1109\/JSEN.2021.3062660","article-title":"Efficient Parallel Branch Network with Multi-Scale Feature Fusion for Real-Time Overhead Power Line Segmentation","volume":"21","author":"Gao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_19","unstructured":"An, D., Zhang, Q., Chao, J., Li, T., Qiao, F., Deng, Y., Bian, Z., and Xu, J. (2023). DUFormer: A Novel Architecture for Power Line Segmentation of Aerial Images. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, Y., Zhang, W., Zhang, H., Bai, D., Li, J., and Xu, R. (2020, January 4\u20137). An Intelligent Infrared Image Fault Diagnosis for Electrical Equipment. Proceedings of the 2020 5th Asia Conference on Power and Electrical Engineering (ACPEE), Chengdu, China.","DOI":"10.1109\/ACPEE48638.2020.9136567"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shams, F., Omar, M., Usman, M., Khan, S., Larkin, S., and Raw, B. (2022, January 27\u201328). Thermal Imaging of Utility Power Lines: A Review. Proceedings of the 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICEET56468.2022.10007289"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G.K., Yaw, C.T., Koh, S.P., Abedin, T., Raj, A.A., Tiong, S.K., and Chen, C.P. (2022). A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations. Energies, 15.","DOI":"10.3390\/en15166000"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liang, Y., Qian, J., Jiang, K., Sun, X., and Huang, L. (2023, January 27\u201330). Review of the Theory and Application of Infrared Thermography in Transmission Line Monitoring and Equipment Monitoring. Proceedings of the 2023 Panda Forum on Power and Energy (PandaFPE), Chengdu, China.","DOI":"10.1109\/PandaFPE57779.2023.10141470"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ullah, I., Khan, R.U., Yang, F., and Wuttisittikulkij, L. (2020). Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment. Energies, 13.","DOI":"10.3390\/en13020392"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, J.S., Choi, K.N., and Kang, S.W. (2021). Infrared Thermal Image-Based Sustainable Fault Detection for Electrical Facilities. Sustainability, 13.","DOI":"10.3390\/su13020557"},{"key":"ref_26","first-page":"8","article-title":"Deep Learning Method and Infrared Imaging as a Tool for Transformer Faults Detection","volume":"6","author":"Nikolovski","year":"2018","journal-title":"J. Electr. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9295771","DOI":"10.1155\/2021\/9295771","article-title":"Thermal Fault Detection and Diagnosis of Electrical Equipment Based on the Infrared Image Segmentation Algorithm","volume":"2021","author":"Li","year":"2021","journal-title":"Adv. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, S., Yang, D., Li, W., Xia, Y., and Tang, Y. (2015, January 8\u201312). Detection and Fault Diagnosis of Power Transmission Line in Infrared Image. Proceedings of the 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China.","DOI":"10.1109\/CYBER.2015.7287976"},{"key":"ref_29","first-page":"6","article-title":"Improved Detection of Fault Diagnosis in High Voltage Transmission Lines Using Thermal Imaging Based Convolutional Neural Network Module","volume":"63","author":"Kalos","year":"2020","journal-title":"Solid State Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1134\/S1054661819010140","article-title":"Visible and Infrared Imaging Based Inspection of Power Installation","volume":"29","author":"Jalil","year":"2019","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting Encoder Representations for Efficient Semantic Segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High-Resolution Satellite Imagery Road Extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_35","unstructured":"K\u00e4lvi\u00e4inen, H., Hirvonen, P., Xu, L., and Oja, E. (2006). Computer Vision\u2014ECCV \u201994, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, H., Ma, Y., Bao, H., and Zhang, Y. (2023). Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments. Appl. Sci., 13.","DOI":"10.20944\/preprints202303.0319.v1"},{"key":"ref_37","unstructured":"Wada, K. (2023, September 01). Labelme: Image Polygonal Annotation with Python. Available online: https:\/\/github.com\/zhong110020\/labelme#labelme-image-polygonal-annotation-with-python."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8441\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:06:20Z","timestamp":1760130380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"references-count":37,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208441"],"URL":"https:\/\/doi.org\/10.3390\/s23208441","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,13]]}}}