{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:16:52Z","timestamp":1771003012679,"version":"3.50.1"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,11,11]],"date-time":"2024-11-11T00:00:00Z","timestamp":1731283200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"China Southern Power Grid Company Limited","award":["090000KK52222180"],"award-info":[{"award-number":["090000KK52222180"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Traditional methods for detecting damage of electric power line insulators are often limited to improve the accuracy of detection because of poor datasets. To enhance the performance of insulator detectors, in this paper, we propose a semi-supervised object detection method by combining a two-stage proposal-connection detection net (TPD-Net) with an enhanced network structure generative adversarial network (En-GAN), termed the noise self-training insulator-defect detection network (NS-IDNet). Firstly, the En-GAN approach is utilized to synthesize a large number of class-balanced samples as unlabeled data, controlled by a coefficient. Secondly, according to the TPD-Net method, a teacher model is trained to employ the labeled data, and then this teacher model is used to predict the sample labels for the unlabeled data. Finally, noise self-training is conducted. The student model and teacher model are repeatedly trained until convergence, while noises are introduced into the above models and samples. Diagnostic results on the test set from the original dataset reveal that the proposed NS-IDNet outperforms the traditional supervised model. Additionally, comparative experiments demonstrate the diagnostic accuracy of the proposed NS-IDNet is superior to traditional semi-supervised models.<\/jats:p>","DOI":"10.1177\/14727978241299599","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T10:26:26Z","timestamp":1745835986000},"page":"67-83","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A noise self-training insulator-defect detection method based on the improved two-stage detection net with data generation"],"prefix":"10.1177","volume":"25","author":[{"given":"Lizhu","family":"Liu","sequence":"first","affiliation":[{"name":"Shenzhen Grid Co., Ltd."}]},{"given":"Wanyi","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenzhen Grid Co., Ltd."}]},{"given":"Pochen","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan University"}]},{"given":"Yitao","family":"Li","sequence":"additional","affiliation":[{"name":"Wuhan University"}]}],"member":"179","published-online":{"date-parts":[[2024,11,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs15194841"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/drones7030202"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/drones7020125"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10921-018-0513-1"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13193971"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3105419"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2320\/1\/012025"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1049\/tje2.12029"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1748\/4\/042012"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106374"},{"key":"e_1_3_2_12_2","first-page":"1137","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren S","year":"2015","unstructured":"Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 2015; 28: 1137\u20131149.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1049\/cit2.12110"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38109-6"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S et al. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition Las Vegas NV 2016 pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2538095"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Karras T Laine S Aittala M et al. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition New Orleans LA 2020 pp. 8110\u20138119.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Richardson E Alaluf Y Patashnik O et al. Encoding in style: a stylegan encoder for image-to-image translation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition New Orleans LA 2021 pp. 2287\u20132296.","DOI":"10.1109\/CVPR46437.2021.00232"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2015974"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3220219"},{"issue":"1","key":"e_1_3_2_21_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava N","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15(1): 1929\u20131958.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.2.219"},{"key":"e_1_3_2_23_2","unstructured":"Pei-Xia S Hui-Ting L Tao L. Learning discriminative CNN features and similarity metrics for image retrieval. In 2016 IEEE International Conference on Signal Processing Communications and Computing (ICSPCC) Hong Kong 2016 pp. 1\u20135. IEEE."},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Lin T-Y Goyal P Girshick R et al. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision Venice 2017 pp. 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Xie Q Luong M-T Hovy E et al. Self-training with noisy student improves imagenet classification. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition New Orleans LA 2020 pp. 10687\u201310698.","DOI":"10.1109\/CVPR42600.2020.01070"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241299599","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978241299599","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241299599","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:07Z","timestamp":1771000267000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978241299599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,11]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/14727978241299599"],"URL":"https:\/\/doi.org\/10.1177\/14727978241299599","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,11]]}}}