{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:13:12Z","timestamp":1750219992664,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,21]]},"DOI":"10.1145\/3573428.3573613","type":"proceedings-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T10:43:09Z","timestamp":1678876989000},"page":"1028-1032","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on YOLOV5 Electric Workers Wear Detection Model Based on Channel Pruning and Attention Fusion"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1038-0652","authenticated-orcid":false,"given":"Chengcheng","family":"Zhu","sequence":"first","affiliation":[{"name":"NARI Group Corporation (State Grid Electric Power Research Institute), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7354-8566","authenticated-orcid":false,"given":"Miao","family":"Gong","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9765-2790","authenticated-orcid":false,"given":"Wang","family":"Luo","sequence":"additional","affiliation":[{"name":"NARI Group Corporation (State Grid Electric Power Research Institute), China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4616-6300","authenticated-orcid":false,"given":"Xinsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5735-7815","authenticated-orcid":false,"given":"Xiaofa","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-3882","authenticated-orcid":false,"given":"Yuying","family":"Shao","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7401-4358","authenticated-orcid":false,"given":"Boyang","family":"Sun","sequence":"additional","affiliation":[{"name":"State Grid Shanghai Municipal Electric Power Company, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4496-7689","authenticated-orcid":false,"given":"Xiaolong","family":"Hao","sequence":"additional","affiliation":[{"name":"NARI Group Corporation (State Grid Electric Power Research Institute), China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2017.08.022"},{"volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE","key":"e_1_3_2_1_2_1","unstructured":"REDMON J, DIVVALA S, GIRSHICK R, You only look once: unified, real-time object detection [C]\/\/ 2016. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788."},{"volume-title":"YOLO9000: better, faster, stronger [C]\/\/ 2017 IEEE Conference on Computer Vision and Pattern Recognition","year":"2017","key":"e_1_3_2_1_3_1","unstructured":"REDMON J, FARHADI A. YOLO9000: better, faster, stronger [C]\/\/ 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6517- 6525."},{"key":"e_1_3_2_1_4_1","first-page":"89","article-title":"an incremental improvement [C]\/\/ 2018 IEEE Conference on Computer Vision and Pattern Recognition","volume":"2018","author":"Ov","unstructured":"REDMON J, FARHADI A. YOLOv3: an incremental improvement [C]\/\/ 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 89-95.","journal-title":"Salt Lake City: IEEE"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Guo J Cheng J Pang J [IEEE 2013 20th IEEE International Conference on Image Processing (ICIP) - Melbourne Australia (2013.09.15-2013.09.18)] 2013 IEEE International Conference on Image Processing - Real-time hand detection based on multi-stage HOG-SVM classifier [J]. 2013: 4108-4111.","DOI":"10.1109\/ICIP.2013.6738846"},{"volume-title":"Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning","author":"Shin H. C.","key":"e_1_3_2_1_6_1","unstructured":"Shin, H. C., Roth, H. R., Gao, M., Le, L., Xu, Z., & Nogues, I., 2016. Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285-1298."},{"key":"e_1_3_2_1_7_1","unstructured":"Elhenawy Mohammed \"Detecting Driver Distraction in the ANDS Data using Pre-trained Models and Transfer Learning.\" The Australasian Road Safety Conference. 2019."},{"key":"e_1_3_2_1_8_1","volume-title":"ImageNet Classification with Deep Convolutional Neural Networks.\" Advances in neural information processing systems 25.2","author":"Krizhevsky A., I.","year":"2012","unstructured":"Krizhevsky, A., I. Sutskever, and G. Hinton. \"ImageNet Classification with Deep Convolutional Neural Networks.\" Advances in neural information processing systems 25.2. 2012."},{"key":"e_1_3_2_1_9_1","volume-title":"Computer Science","author":"Simonyan K","year":"2014","unstructured":"Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition [J]. Computer Science, 2014."},{"key":"e_1_3_2_1_10_1","volume-title":"A Residual Network of Water Scene Recognition Based on Optimized Inception Module and Convolutional Block Attention Module [C]\/\/ 2019 6th International Conference on Systems and Informatics (ICSAI)","author":"Xie L","year":"2019","unstructured":"Xie L, Huang C. A Residual Network of Water Scene Recognition Based on Optimized Inception Module and Convolutional Block Attention Module [C]\/\/ 2019 6th International Conference on Systems and Informatics (ICSAI). 2019."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims-20-1302"},{"key":"e_1_3_2_1_12_1","volume-title":"Application & Services (HEALTHCOM). IEEE","author":"Hao S","year":"2021","unstructured":"Hao S, Wang R, Wang Y, A Spatial Attention based Convolutional Neural Network for Gesture recognition with HD-sEMG signals [C]\/\/ 2020 IEEE International Conference on E-health Networking, Application & Services (HEALTHCOM). IEEE, 2021."},{"volume-title":"CBAM: convolutional block attention module[C]\/\/15th European Conference on Computer Vision","year":"2018","key":"e_1_3_2_1_13_1","unstructured":"WOO S, PARK J, LEE J Y, CBAM: convolutional block attention module[C]\/\/15th European Conference on Computer Vision, 2018: 3-19."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"e_1_3_2_1_15_1","volume-title":"Narang S","author":"Han S","year":"2016","unstructured":"Han S, Pool J, Narang S, DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow [J]. 2016."},{"key":"e_1_3_2_1_16_1","volume-title":"Deep Neural Network VALIDATION.","author":"Somefun O A","year":"2017","unstructured":"Somefun O A. Visualization: RMSProp BatchNorm+Drop, Deep Neural Network VALIDATION. 2017."},{"volume-title":"Sparse networks from scratch: faster training without losing performance [J]. arXiv","year":"1907","key":"e_1_3_2_1_17_1","unstructured":"DETTMERS T, ZETTLEMOYER L. Sparse networks from scratch: faster training without losing performance [J]. arXiv: 1907.04840, 2019."},{"volume-title":"Pruning filters for efficient convnets [J]. arXiv:1608.08710","year":"2016","key":"e_1_3_2_1_18_1","unstructured":"LI H, KADAV A, DURDANOVIC I, Pruning filters for efficient convnets [J]. arXiv:1608.08710, 2016."}],"event":{"name":"EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering","acronym":"EITCE 2022","location":"Xiamen China"},"container-title":["Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3573428.3573613","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3573428.3573613","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:33Z","timestamp":1750182573000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3573428.3573613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"references-count":18,"alternative-id":["10.1145\/3573428.3573613","10.1145\/3573428"],"URL":"https:\/\/doi.org\/10.1145\/3573428.3573613","relation":{},"subject":[],"published":{"date-parts":[[2022,10,21]]},"assertion":[{"value":"2023-03-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}