An insulator defect detection method for transmission lines in complex weather conditions based on improved YOLOv8n.

Addressing challenges in UAV power line inspection-where insulator defect detection models are prone to environmental interference, insufficient feature representation, and difficulty balancing lightweight requirements-this study develops a lightweight image defect detection model that integrates high accuracy with strong robustness. An enhanced algorithm based on YOLOv8n is proposed. MobileNetV4 is adopted as the lightweight backbone, CBAM is introduced to enhance defect feature representation, ABIFPN is designed for multiscale bidirectional feature fusion, and SIoU is employed to improve localization accuracy. A multi-weather dataset containing 3,851 images of self-shattered and damaged insulators under rainy, snowy, foggy, overcast, and varying-exposure conditions was constructed using real and synthesized images. The dataset was divided into training, validation, and test sets at a ratio of 7:2:1. Across five independent experiments, the proposed model improved precision, recall, mAP@0.5, and mAP@0.5:0.95 by 2.43, 2.32, 2.43, and 5.47 percentage points, respectively, compared with the baseline. With a model size of only 7.01 MB, demonstrated better overall detection performance than YOLOv5n and YOLOv7-tiny. These results indicate its potential for UAV-mounted edge-based transmission-line inspection. However, some weather samples were synthetically generated, and more real-world data will be incorporated in future work.