{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:40:45Z","timestamp":1702600845264},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>The detection of transmission line fittings is extremly significant for keeping the operation of the power grid safe and stable. In order to avoid the occurrence of poor detection results caused by the complicated background environment and different shapes and sizes of the detected targets in aerial photographs of transmission lines, an enhanced RetinaNet method for detecting transmission line fittings is proposed. The proposed approach incorporates an attention mechanism and multi-scale feature fusion, called AMF-RetinaNet. First, a RetinaNet detection network has been built. To enhance the significance of the detected fittings, a spatial and channel convolutional attention model is introduced to suppress complicated background interference. Then, the FPN structure of the original RetinaNet detection structure Neck is changed to the FPN+PAN structure, thereby obtaining more comprehensive multi-scale features. Finally, experiments on the self-built fittings dataset verified the feasibility of AMF-RetinaNet. Experimental results demonstrate that AMF-RetinaNet can effectively detect multi-scale fitting targets on transmission lines, even in challengin environment. Compared with standard RetinaNet, its average detection accuracy mAP has increased by 4.81%.<\/jats:p>","DOI":"10.3233\/faia231026","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:06:23Z","timestamp":1702566383000},"source":"Crossref","is-referenced-by-count":0,"title":["AMF-RetinaNet: Improved RetinaNet for Transmission Line Fittings Detection Based on Attention Mechanism and Multi-Scale Feature Fusion"],"prefix":"10.3233","author":[{"given":"Xudong","family":"Chen","sequence":"first","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Han","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghao","family":"Xiao","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoguo","family":"Xue","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liuyang","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Ding","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha Power Supply Bureau of Guangdong Power Grid Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:06:25Z","timestamp":1702566385000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231026","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}