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In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.<\/jats:p>","DOI":"10.1155\/2021\/9976209","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T22:05:09Z","timestamp":1620943509000},"page":"1-9","source":"Crossref","is-referenced-by-count":6,"title":["Application of Data-Driven Iterative Learning Algorithm in Transmission Line Defect Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3128-9727","authenticated-orcid":true,"given":"Yuquan","family":"Chen","sequence":"first","affiliation":[{"name":"Jiangsu Frontier Electric Technology, Nanjing 211102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4309-517X","authenticated-orcid":true,"given":"Hongxing","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Frontier Electric Technology, Nanjing 211102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7026-2431","authenticated-orcid":true,"given":"Jie","family":"Shen","sequence":"additional","affiliation":[{"name":"Jiangsu Frontier Electric Technology, Nanjing 211102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9958-068X","authenticated-orcid":true,"given":"Xingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiangsu Frontier Electric Technology, Nanjing 211102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6803-7756","authenticated-orcid":true,"given":"Xiaowei","family":"Gao","sequence":"additional","affiliation":[{"name":"Beijing Imperial Image Intelligent Technology, Beijing 100085, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"3018","article-title":"Target detection method based on convolutional neural network for SAR image","volume":"38","author":"L. 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