{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:16:05Z","timestamp":1771002965254,"version":"3.50.1"},"reference-count":23,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p>Aiming at the problems of insulator detection such as low recognition accuracy for small targets and the algorithm model is too large and difficult to be deployed to the edge devices. In this paper, a lightweight YOLOv8-ASF-P2 insulator defect detection model is designed. The model introduces the ASF and P2 detection layer, and at the same time according to the idea of ASF to add the P2 detection layer, the new network structure is trained and then pruned. After pruning, the mAP of this algorithm is 89.5%, the model size is 2.1\u00a0MB, and the detection speed is 144.9FPS. 1.7% improvement in mAP, 64.7% reduction in model size, and 40% improvement in detection speed compared with the YOLOv8 algorithm, which verifies the effectiveness of the improved method.<\/jats:p>","DOI":"10.1177\/14727978241304269","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T04:22:21Z","timestamp":1738297341000},"page":"3993-4003","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Study on defect detection in lightweight insulators based on improved YOLOv8"],"prefix":"10.1177","volume":"24","author":[{"given":"Pengfei","family":"Wang","sequence":"first","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd."}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd."}]},{"given":"Xinyun","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd."}]},{"given":"Ming","family":"Tang","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd."}]},{"given":"Yaqiao","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd."}]}],"member":"179","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06792-z"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3073422"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12031207"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982288"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3238524"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12071204"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Liao G-P Yang G-J Tong W-T et al. (eds) Study on power line insulator defect detection via improved faster region-based convolutional neural network. In: 2019 IEEE 7th international conference on computer science and network technology (ICCSNT) Dalian China 19\u201320 October 2019. IEEE.","DOI":"10.1109\/ICCSNT47585.2019.8962497"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3305667"},{"issue":"4","key":"e_1_3_2_10_2","first-page":"474","article-title":"An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images","volume":"5","author":"Ling Z","year":"2019","unstructured":"Ling Z, Zhang D, Qiu RC, et al. An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images. CSEE Journal of Power and Energy Systems 2019; 5(4): 474\u2013482.","journal-title":"CSEE Journal of Power and Energy Systems"},{"key":"e_1_3_2_11_2","unstructured":"Nazir A Wani MA (eds) You only Look once - object detection models: a review. In: 17th INDIACom; 2023 10th International Conference on Computing for Sustainable Global Development INDIACom 2023 New Delhi India 15\u201317 March 2023. Institute of Electrical and Electronics Engineers Inc."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02798-1"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3220285"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/e23121587"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Huang S Dong X Wang Y et al. (eds) Detection of insulator burst position of lightweight YOLOv5. In: Proceedings of the 8th International Conference on Computing and Artificial Intelligence 2022.","DOI":"10.1145\/3532213.3532300"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/s22228801"},{"issue":"6","key":"e_1_3_2_17_2","first-page":"2020","article-title":"Power vision edge intelligence: power depth vision acceleration technology driven by edge computing","volume":"44","author":"Ma F","year":"2020","unstructured":"Ma F, Wang B, Dong X, et al. Power vision edge intelligence: power depth vision acceleration technology driven by edge computing. Dianwang Jishu\/Power System Technology 2020; 44(6): 2020\u20132029.","journal-title":"Dianwang Jishu\/Power System Technology"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.13009"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2023.3328178"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-023-01401-9"},{"key":"e_1_3_2_21_2","unstructured":"Ge Z Liu S Wang F et al. YOLOX: exceeding YOLO series in 2021. 2021."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17245-1"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Kang M Ting C-M Ting FF et al. ASF-YOLO: a novel YOLO model with attentional scale sequence fusion for cell instance segmentation. 2023.","DOI":"10.1016\/j.imavis.2024.105057"},{"key":"e_1_3_2_24_2","unstructured":"Lee J Park S Mo S et al. (eds) Layer-adaptive sparsity for the magnitude-based pruning. In: 9th International Conference on Learning Representations ICLR 2021 3\u20137 May 2021. Virtual Online: International Conference on Learning Representations ICLR."}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241304269","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978241304269","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241304269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:08Z","timestamp":1771000268000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978241304269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["10.1177\/14727978241304269"],"URL":"https:\/\/doi.org\/10.1177\/14727978241304269","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}