{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:03:57Z","timestamp":1789707837489,"version":"4.0.1"},"reference-count":36,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhiliang Zhu","award":["2025C01033"],"award-info":[{"award-number":["2025C01033"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges of low localization accuracy for insulator defects in complex inspection scenarios and excessive model redundancy that hinders edge deployment, this paper proposes FS-YOLO, a lightweight defect detection algorithm. First, a C3k2 ConvFormer Gated Linear Unit (C3k2-CFGLU) module replaces the original backbone, using depthwise separable convolution and gated linear units to enhance defect feature representation while reducing computation. Second, a Multi-Branch Multi-Scale Feature Pyramid Network (MBMSFPN) neck improves defect target perception via multi branch auxiliary connections that strengthen high-level and low-level feature interaction. Third, a Shared Convolution Detection Head (SCDH) reduces parameters through multi-scale shared convolutions and group normalization, and incorporates a localization quality estimation mechanism to offset lightweight induced accuracy loss. Finally, layer adaptive sparsity for magnitude-based pruning (LAMP) removes redundant channels, compressing model size and improving efficiency. Experiments show that FS-YOLO achieves 93.0% precision, 88.3% recall, 92.9% mAP@0.5, and 62.9% mAP@0.5:0.95, with only 0.88M parameters and 4.1 GFLOPs. Compared with the baseline, parameters and computation drop by 65.9% and 34.9%, while mAP@0.5 increases by 5.7%. Against other mainstream YOLO variants, FS-YOLO offers superior accuracy efficiency trade offs, offering a promising reference for the intelligent development of power line inspection.<\/jats:p>","DOI":"10.3390\/s26185876","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:07:29Z","timestamp":1789574849000},"page":"5876","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FS-YOLO: A Lightweight Insulator Defect Detection Method Based on Multi-Level Feature Fusion and Localization Quality Estimation"],"prefix":"10.3390","volume":"26","author":[{"given":"Congjie","family":"Wen","sequence":"first","affiliation":[{"name":"College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China"},{"name":"Wenzhou Key Laboratory of Unmanned Intelligent Systems and Equipment, Wenzhou University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiliang","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China"},{"name":"Wenzhou Key Laboratory of Unmanned Intelligent Systems and Equipment, Wenzhou University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijian","family":"Weng","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China"},{"name":"Wenzhou Key Laboratory of Unmanned Intelligent Systems and Equipment, Wenzhou University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zekai","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China"},{"name":"Wenzhou Key Laboratory of Unmanned Intelligent Systems and Equipment, Wenzhou University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325000, China"},{"name":"Wenzhou Key Laboratory of Unmanned Intelligent Systems and Equipment, Wenzhou University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1049\/gtd2.12916","article-title":"Insulator defect detection with deep learning: A survey","volume":"17","author":"Liu","year":"2023","journal-title":"IET Gener. 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