Insulator self-blasts, cracked insulators, and bird nests often lead to large-scale power outages and safety accidents, while the detection system based on a single UAV and YOLOv7 is difficult to meet the speed and accuracy requirements in actual detection. Therefore, a novel insulator defect detection method based on improved YOLOv7 and a multi-UAV collaborative system is proposed innovatively. Firstly, a complete insulator defects dataset is constructed, and the introduction of insulator self-blasts, cracked insulators, and bird nest images avoids the problem of low reliability for single defect detection. Secondly, a multi-UAV collaborative platform is proposed, which improves the search scope and efficiency. Most critically, an improved YOLOv7-C3C2-GAM is proposed. The introduction of the C3C2 module and the CNeB2 structure improves the efficiency and accuracy of feature extraction, and the introduction of a global attention mechanism (GAM) improved the feature extraction ability to extract key information about small targets or occluded targets and feature in the region of interest. Compared with YOLOv7, the accuracies of YOLOv7-C3C2 and YOLOv7-C3C2-GAM are improved by 1.3% and 0.5%, respectively, the speed of YOLOv7-C3C2 is improved by 0.1 ms, and the lightweight sizes are reduced by 8.2 Mb and 8.1 Mb, respectively. Therefore, the proposed method provides theoretical and technical support for power equipment defect detection.

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