{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:54:10Z","timestamp":1781016850055,"version":"3.54.1"},"reference-count":9,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFB3206800"],"award-info":[{"award-number":["2022YFB3206800"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>To supply uninterrupted electric power is extremely crucial for the economy and daily life. The traditional manual inspection method for power transmission line fault detection has many disadvantages. Although deep learning has been applied in power transmission line detection, existing schemes have deficiencies in complex environment with low-resolution images and small target. To solve this problem, this paper proposes a visual feature modeling method based on graph convolution and constructs a graph neural network model, HSPAN-GNN (High-priority Subsampling with SPD and Attention Normalization-Graph Neural Network), which combines graph convolution and convolution operations. We build a novel HSPAN-GNN model includes a high-degree priority subsampling module to balance computational efficiency, memory overhead, and accelerate inference. The proposed Space-to-Depth Convolution (SPD-Conv) solves the detection problems in small target and low-resolution scenarios, and the Normalization-based Attention Module (NAM) enhances the detection performance. Experiments have shown that the HSPAN-GNN model can achieve efficient and accurate target detection of transmission lines.<\/jats:p>","DOI":"10.1186\/s13634-025-01251-6","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:49:00Z","timestamp":1758880140000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HSPAN-GNN-based fault detection for power transmission lines"],"prefix":"10.1186","volume":"2025","author":[{"given":"Ximing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiguang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"1251_CR1","doi-asserted-by":"crossref","unstructured":"Zhou, Yuhuai, et al. \"Application and Research of Image Recognition Technology in Transmission Line Loss Detection.\" 2024 8th International conference on electrical, mechanical and computer engineering (ICEMCE). IEEE, 2024.","DOI":"10.1109\/ICEMCE64157.2024.10862973"},{"key":"1251_CR2","doi-asserted-by":"crossref","unstructured":"Junfeng, Li, Li Min, and Wang Qinruo. \"A novel insulator detection method for aerial images.\" Proceedings of the 9th International conference on computer and automation engineering. 2017.","DOI":"10.1145\/3057039.3057065"},{"key":"1251_CR3","unstructured":"M. Sen et al., \"Research on Insulator Defects in Power Inspection\", Mathematical Modeling and Its Application, 2020."},{"key":"1251_CR4","doi-asserted-by":"publisher","first-page":"130410","DOI":"10.1109\/ACCESS.2021.3110159","volume":"9","author":"HA Foudeh","year":"2021","unstructured":"H.A. Foudeh, P.-K. Luk, J.F. Whidborne, An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: a comprehensive review. IEEE Access 9, 130410\u2013130433 (2021)","journal-title":"IEEE Access"},{"key":"1251_CR5","doi-asserted-by":"crossref","unstructured":"Ambatkar, Harshita P., and Rajendra K. Dhatrak. \"Drone applications in transmission line.\" In: 2022 International Mobile and Embedded Technology Conference (MECON). IEEE, 2022.","DOI":"10.1109\/MECON53876.2022.9752257"},{"key":"1251_CR6","unstructured":"H. Zhu et al., \"Bird\u2019s nest detection method for transmission lines based on improved YOLOv5\". Electronic Design Engineering, 2024."},{"key":"1251_CR7","unstructured":"Y. Chen et al., \"U-Net-based Transmission Line Segmentation Network\", Electric Engineering, 2024."},{"key":"1251_CR8","unstructured":"C. Liu et al., \"Research Progress of Deep Learning Methods for Insulator Defect Detection in UAV based Aerial Images\", Transactions of China Electrotechnical Society, 2024."},{"issue":"3","key":"1251_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/rs15030865","volume":"15","author":"Z Li","year":"2023","unstructured":"Z. Li et al., Design and application of a UAV autonomous inspection system for high-voltage power transmission lines. Remote Sens. 15(3), 865 (2023)","journal-title":"Remote Sens."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-025-01251-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-025-01251-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-025-01251-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T09:49:07Z","timestamp":1758880147000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-025-01251-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,26]]},"references-count":9,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1251"],"URL":"https:\/\/doi.org\/10.1186\/s13634-025-01251-6","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,26]]},"assertion":[{"value":"27 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"43"}}