Abstract:

The YOLO objective detection algorithm is currently the mainstream method for detecting insulator defects in image-based power transmission lines. However, due to the high complexity of existing models, a reasonable and effective parameter compression method is urgently needed as a prerequisite to establish the foundation for solving the dilemma of UAV edge device deployment. Additionally, the complex background of the insulator defect images captured by drones and small size of defects can lead to problems such as false detections and omissions. To address these issues, the Insulator Defect Detection-YOLOv7 (IDD-YOLOv7) model was proposed for multi-defect detection in power transmission line insulators, aiming to reduce model complexity and enhance robustness. Firstly, a coordinate attention mechanism was incorporated during the multi-scale feature fusion process to suppress interference from complex backgrounds and enhance the model’s global perception of small objects. Secondly, a C3GhostNetV2 module was designed to capture long-range dependencies between different spatial pixels, thus enhancing the model’s expressive power while reducing the parameter quantity and floating-point operation complexity. Lastly, the Focal-CIoU loss function was proposed to improve the contribution of high-quality anchors to the model and accelerate model convergence. Experimental results demonstrated that compared with the baseline model, the mAP⁵⁰ of this method has increased by 3.8%, with precision and recall rates increasing by 1.7% and 7.6%, respectively, and the parameter quantity and floating-point operations have decreased by 18.3% and 14.0%, respectively. The AP⁵⁰ of insulator self-explosion, damage, and flashover defects have increased by 0.8%, 4.5%, and 6.3%, respectively.

Key words: YOLOv7, insulator defect detection, attention mechanism, model complexity, lightweight, loss function