FS-YOLO: A Lightweight Insulator Defect Detection Method Based on Multi-Level Feature Fusion and Localization Quality Estimation

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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.