{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:35:54Z","timestamp":1778574954445,"version":"3.51.4"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":54,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFB4204800"],"award-info":[{"award-number":["2024YFB4204800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>To address the requirements for high\u2010precision detection of transmission line defects by inspection drones in low\u2010light environments such as cloudy days and to overcome the problem of significant accuracy degradation in current defect detection algorithms under low\u2010light conditions, this paper uses YOLOv8 as the baseline algorithm. By introducing the PENet low\u2010light enhancement network, the Slim\u2010Neck lightweight neck network, and the Wise\u2010IoU loss function, it forms the low\u2010light line defect detection method LCDD\u2010YOLO. Simultaneously, to address the current lack of images in low\u2010light environments, this paper explores a low\u2010light image generation scheme based on the CycleGAN method and constructs the comprehensive transmission line defect dataset (TLCDD). Dataset comparison experiments verified the effectiveness of the TLCDD dataset constructed in this paper in improving the accuracy of algorithm defect detection in low light. Through ablation experiments, compared with the baseline YOLOv8 algorithm, LCDD\u2010YOLO achieves a 10.583% improvement in mAP@0.5 while incurring only a small increase of 2.9 GFLOPs and a slight decrease of 5 FPS. Furthermore, in comparative experiments, LCDD\u2010YOLO demonstrated the highest defect recognition accuracy in low\u2010light environments, proving its superior performance and its ability to meet the demand for detecting transmission line defects in low\u2010light environments.<\/jats:p>","DOI":"10.1155\/int\/2674074","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T22:14:58Z","timestamp":1773008098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Transmission Line Defect Detection in Low\u2010Light Environments Using Image Adversarial Synthesis and the LCDD\u2010YOLO Algorithm"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0335-9443","authenticated-orcid":false,"given":"Yuxin","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9496-9386","authenticated-orcid":false,"given":"Shaotong","family":"Pei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2017.12.016"},{"key":"e_1_2_10_2_2","article-title":"Real-Time Defect Detection Method for Transmission Line Edge End Based on LEE-YOLOv7","volume":"1","author":"Hu C.","year":"2024","journal-title":"High Voltage Engineering"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/ese3.70136"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2025.112334"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/ese3.70107"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3418082"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2024.3350162"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/s24020565"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.13191"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2024.110464"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13020305"},{"key":"e_1_2_10_12_2","first-page":"2825","article-title":"The Defect Detection Method for Cross-Environment Power Transmission Line Based on ER-YOLO Algorithm","volume":"39","author":"Shaotong P.","year":"2024","journal-title":"Transactions of China Electrotechnical Society"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1049\/hve2.12513"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3453332"},{"key":"e_1_2_10_15_2","first-page":"2867","article-title":"Disc Insulator Defect Detection Based on Mixed Sample Transfer Learning","volume":"43","author":"Zhai Y.","year":"2023","journal-title":"Proceedings of the CSEE"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.13336\/j.10036520.hve.20231241"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-44195-0_14"},{"key":"e_1_2_10_18_2","doi-asserted-by":"crossref","unstructured":"VargheseR.andYolov8M. 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