{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:01:11Z","timestamp":1780765271323,"version":"3.54.1"},"reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.aei.2026.104840","type":"journal-article","created":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T01:12:05Z","timestamp":1779585125000},"page":"104840","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Limited-sample deep learning-based detection and segmentation of infrared radiation anomaly zones in rock fractures"],"prefix":"10.1016","volume":"75","author":[{"given":"Shaokang","family":"Shang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.aei.2026.104840_b1","article-title":"Applications and advancements of infrared thermal imaging technology in geotechnical engineering: a review","author":"Yang","year":"2025","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"10.1016\/j.aei.2026.104840_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.enggeo.2019.105470","article-title":"Identifying and mapping potentially adverse discontinuities in underground excavations using thermal and multispectral UAV imagery","volume":"266","author":"Turner","year":"2020","journal-title":"Eng. Geol."},{"issue":"1","key":"10.1016\/j.aei.2026.104840_b3","doi-asserted-by":"crossref","first-page":"14682","DOI":"10.1038\/s41598-024-65527-x","article-title":"Infrared thermography reveals weathering hotspots at the Po\u017e\u00e1ry field laboratory","volume":"14","author":"Loche","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104840_b4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.compag.2017.05.001","article-title":"An overview of current and potential applications of thermal remote sensing in precision agriculture","volume":"139","author":"Khanal","year":"2017","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.aei.2026.104840_b5","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.aej.2017.01.020","article-title":"Crack detection using image processing: A critical review and analysis","volume":"57","author":"Mohan","year":"2018","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.aei.2026.104840_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2022.102632","article-title":"Experimental study on precursor characteristics of rock failure based on strain and temperature changes","volume":"41","author":"Kong","year":"2023","journal-title":"Case Stud. Therm. Eng."},{"issue":"3","key":"10.1016\/j.aei.2026.104840_b7","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3390\/infrastructures3030036","article-title":"Condition assessment of reinforced concrete bridges: Current practice and research challenges","volume":"3","author":"Omar","year":"2018","journal-title":"Infrastructures"},{"key":"10.1016\/j.aei.2026.104840_b8","series-title":"2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference","first-page":"539","article-title":"Adaptive image edge detection model using improved Canny algorithm","author":"Kong","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111248","article-title":"Automatic detection of asphalt pavement thickness: A method combining GPR images and improved Canny algorithm","volume":"196","author":"Wang","year":"2022","journal-title":"Measurement"},{"issue":"7","key":"10.1016\/j.aei.2026.104840_b10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0288792","article-title":"Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy","volume":"18","author":"Abdushkour","year":"2023","journal-title":"PLoS One"},{"key":"10.1016\/j.aei.2026.104840_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2021.103036","article-title":"Region adaptive morphological reconstruction fuzzy C-means for near-field 3-D SAR image target extraction","volume":"113","author":"Li","year":"2021","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.aei.2026.104840_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.102189","article-title":"SISG-net: Simultaneous instance segmentation and grasp detection for robot grasp in clutter","volume":"58","author":"Yan","year":"2023","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104840_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102727","article-title":"Deep learning-powered visual inspection for metal surfaces \u2013 impact of annotations on algorithms based on defect characteristics","volume":"62","author":"Dubey","year":"2024","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104840_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103224","article-title":"Deep learning-based rebar detection and instance segmentation in images","volume":"65","author":"Sun","year":"2025","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104840_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103318","article-title":"A real-time welding defect detection framework based on RT-DETR deep neural network","volume":"65","author":"Liu","year":"2025","journal-title":"Adv. Eng. Inf."},{"issue":"4","key":"10.1016\/j.aei.2026.104840_b16","doi-asserted-by":"crossref","first-page":"5288","DOI":"10.1109\/TCSVT.2025.3626574","article-title":"It takes two: Multi-frequency perception with complementary fusion network for complex scene segmentation","volume":"36","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.aei.2026.104840_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfracmech.2025.110811","article-title":"Spatiotemporal early prediction of rock damage in rock engineering based on infrared radiation monitoring technology","volume":"315","author":"Gao","year":"2025","journal-title":"Eng. Fract. Mech."},{"key":"10.1016\/j.aei.2026.104840_b18","series-title":"Path aggregation network for instance segmentation","author":"Liu","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.104067","article-title":"AI-enabled defect detection in industrial products: A comprehensive survey, key insights and future research challenges","volume":"69","author":"Nahar","year":"2026","journal-title":"Adv. Eng. Inf."},{"issue":"1","key":"10.1016\/j.aei.2026.104840_b20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","article-title":"Deep learning models for digital image processing: a review","volume":"57","author":"Archana","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.aei.2026.104840_b21","series-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"key":"10.1016\/j.aei.2026.104840_b22","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"vol. 28","author":"Ren","year":"2015"},{"key":"10.1016\/j.aei.2026.104840_b23","series-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2016"},{"issue":"5","key":"10.1016\/j.aei.2026.104840_b24","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","article-title":"Cascade R-CNN: High quality object detection and instance segmentation","volume":"43","author":"Cai","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.aei.2026.104840_b25","series-title":"Hybrid task cascade for instance segmentation","author":"Chen","year":"2019"},{"key":"10.1016\/j.aei.2026.104840_b26","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/TMM.2021.3070138","article-title":"Deep-irtarget: An automatic target detector in infrared imagery using dual-domain feature extraction and allocation","volume":"24","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.aei.2026.104840_b27","first-page":"1","article-title":"A benchmark and frequency compression method for infrared few-shot object detection","volume":"63","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.aei.2026.104840_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2022.104401","article-title":"A review on 2D instance segmentation based on deep neural networks","volume":"120","author":"Gu","year":"2022","journal-title":"Image Vis. Comput."},{"issue":"3","key":"10.1016\/j.aei.2026.104840_b29","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"10.1016\/j.aei.2026.104840_b30","first-page":"123","article-title":"An improved YOLOv8s model for instance segmentation of rock fracture infrared anomalies","volume":"75","author":"Wang","year":"2025","journal-title":"J. Manuf. Syst."},{"issue":"4","key":"10.1016\/j.aei.2026.104840_b31","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.3390\/s23042204","article-title":"A systematic review of advanced sensor technologies for non-destructive testing and structural health monitoring","volume":"23","author":"Hassani","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104840_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.deepre.2025.100207","article-title":"Infrared thermal image detection method of stressed sandstone fracture based on deep learning","volume":"3","author":"Sun","year":"2026","journal-title":"Deep. Resour. Eng."},{"issue":"12","key":"10.1016\/j.aei.2026.104840_b33","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s12665-020-09052-w","article-title":"Monitoring of the soil moisture regime of an earth-filled dam by means of electrical resistance tomography, close range photogrammetry, and thermal imaging","volume":"79","author":"Zumr","year":"2020","journal-title":"Env. Earth Sci."},{"key":"10.1016\/j.aei.2026.104840_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.tust.2024.106093","article-title":"Utilizing infrared thermography for the condition assessment of tunnel lining with tiled surface in various temperature conditions","volume":"154","author":"Behbahani","year":"2024","journal-title":"Tunn. Undergr. Space Technol."},{"issue":"1","key":"10.1016\/j.aei.2026.104840_b35","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10462-023-10631-z","article-title":"Deep learning models for digital image processing: a review","volume":"57","author":"Archana","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.aei.2026.104840_b36","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.aei.2017.05.001","article-title":"A performance benchmark over semantic rule checking approaches in construction industry","volume":"33","author":"Pauwels","year":"2017","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104840_b37","doi-asserted-by":"crossref","first-page":"9527","DOI":"10.1109\/TMM.2024.3394681","article-title":"Part-aware correlation networks for few-shot learning","volume":"26","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.aei.2026.104840_b38","first-page":"1","article-title":"ADP: Graph adaptive pooling based on edge understanding with graph pooling information bottleneck","author":"Cao","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.aei.2026.104840_b39","first-page":"1","article-title":"Dif-CDFusion: A diffusion-based common\u2013differential network for infrared and visible image fusion","volume":"63","author":"Liu","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.aei.2026.104840_b40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3182664","article-title":"Frequency-domain feature enhancement and cascade refinement for infrared small target detection","volume":"19","author":"Bao","year":"2022","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"10.1016\/j.aei.2026.104840_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101971","article-title":"TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation","volume":"93","author":"Du","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.aei.2026.104840_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2022.135499","article-title":"Prediction and optimization of nitrogen losses in co-composting process by using a hybrid cascaded prediction model and genetic algorithm","volume":"437","author":"Kabak","year":"2022","journal-title":"Chem. Eng. J."},{"key":"10.1016\/j.aei.2026.104840_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.107051","article-title":"A tucker decomposition based knowledge distillation for intelligent edge applications","volume":"101","author":"Dai","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.aei.2026.104840_b44","first-page":"213","article-title":"IFENet: An infrared-visible image fusion network with integrated gating mechanism for feature enhancement","volume":"92","author":"Li","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.aei.2026.104840_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110275","article-title":"A dual residual dense network for image denoising","volume":"147","author":"Batool","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104840_b46","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.jngse.2018.05.019","article-title":"Experimental study on the infrared thermal imaging of a coal fracture under the coupled effects of stress and gas","volume":"55","author":"Li","year":"2018","journal-title":"J. Nat. Gas Sci. Eng."},{"key":"10.1016\/j.aei.2026.104840_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.infrared.2025.105816","article-title":"Infrared radiation characteristics of heterogeneous granite fracture under compression","volume":"147","author":"Sun","year":"2025","journal-title":"Infrared Phys. Technol."},{"key":"10.1016\/j.aei.2026.104840_b48","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","first-page":"113","article-title":"AutoAugment: Learning augmentation strategies from data","author":"Cubuk","year":"2019"},{"key":"10.1016\/j.aei.2026.104840_b49","series-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops","first-page":"702","article-title":"RandAugment: Practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.aei.2026.104840_b50","series-title":"Int. Conf. Learn. Represent.","article-title":"Mixup: Beyond empirical risk minimization","author":"Zhang","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b51","series-title":"Proc. IEEE Int. Conf. Comput. Vis.","first-page":"6023","article-title":"CutMix: Regularization strategy to make strong classifiers and localization better","author":"Yun","year":"2019"},{"key":"10.1016\/j.aei.2026.104840_b52","series-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"issue":"12","key":"10.1016\/j.aei.2026.104840_b53","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.aei.2026.104840_b54","series-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b55","series-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.aei.2026.104840_b56","series-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2014"},{"key":"10.1016\/j.aei.2026.104840_b57","series-title":"The pascal visual object classes (VOC) challenge","author":"Everingham","year":"2010"},{"key":"10.1016\/j.aei.2026.104840_b58","series-title":"Automatic combination of sample selection strategies for few-shot learning","author":"Pecher","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b59","series-title":"Ultralytics YOLOv5","author":"Jocher","year":"2020"},{"key":"10.1016\/j.aei.2026.104840_b60","series-title":"YOLOv6: A single-stage object detection framework for industrial applications","author":"Li","year":"2022"},{"key":"10.1016\/j.aei.2026.104840_b61","series-title":"Ultralytics YOLOv8","author":"Jocher","year":"2023"},{"key":"10.1016\/j.aei.2026.104840_b62","series-title":"YOLOv9: Learning what you want to learn using programmable gradient information","author":"Wang","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b63","series-title":"Ultralytics YOLOv11","author":"Jocher","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b64","series-title":"YOLOv12: Attention-centric real-time object detectors","author":"Tian","year":"2025"},{"key":"10.1016\/j.aei.2026.104840_b65","series-title":"Fast R-CNN","author":"Girshick","year":"2015"},{"key":"10.1016\/j.aei.2026.104840_b66","series-title":"DETRs beat YOLOs on real-time object detection","author":"Zhao","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b67","series-title":"DEIM: DETR with improved matching for fast convergence","author":"Huang","year":"2025"},{"key":"10.1016\/j.aei.2026.104840_b68","series-title":"Ultralytics YOLOv8-seg: Instance segmentation","author":"Jocher","year":"2023"},{"key":"10.1016\/j.aei.2026.104840_b69","series-title":"Ultralytics YOLOv11-seg: Instance segmentation","author":"Jocher","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b70","series-title":"Mask R-CNN","author":"He","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b71","series-title":"Masked-attention mask transformer for universal image segmentation","author":"Cheng","year":"2022"},{"key":"10.1016\/j.aei.2026.104840_b72","series-title":"Proc. ACM Conf. Reproducibility Replicability","first-page":"37","article-title":"On reporting robust and trustworthy conclusions from model comparison studies involving neural networks and randomness","author":"Gundersen","year":"2023"},{"key":"10.1016\/j.aei.2026.104840_b73","series-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2020"},{"key":"10.1016\/j.aei.2026.104840_b74","series-title":"Learning transferable architectures for scalable image recognition","author":"Zoph","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b75","unstructured":"S. Ravi, H. Larochelle, Optimization as a Model for Few-Shot Learning, in: Int. Conf. Learn. Represent., 2016."},{"key":"10.1016\/j.aei.2026.104840_b76","series-title":"The marginal value of adaptive gradient methods in machine learning","author":"Wilson","year":"2018"},{"key":"10.1016\/j.aei.2026.104840_b77","series-title":"YOLOv10: Real-time end-to-end object detection","author":"Wang","year":"2024"},{"key":"10.1016\/j.aei.2026.104840_b78","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.neunet.2023.10.039","article-title":"A survey on few-shot class-incremental learning","volume":"169","author":"Tian","year":"2024","journal-title":"Neural Netw."}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147403462600532X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147403462600532X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:35:58Z","timestamp":1780763758000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S147403462600532X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":78,"alternative-id":["S147403462600532X"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104840","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Limited-sample deep learning-based detection and segmentation of infrared radiation anomaly zones in rock fractures","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104840","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104840"}}