DGNCA-Net: A Lightweight and Efficient Insulator Defect Detection Model

This paper proposes a lightweight DGNCA-Net insulator defect detection algorithm based on improvements to the YOLOv11 framework, addressing the issues of high computational complexity and low detection accuracy for small targets in machine vision-based insulator defect detection methods. Firstly, to enhance the model’s ability to perceive multi-scale targets while reducing computational overhead, a lightweight Ghost-backbone network is designed. This network integrates the improved Ghost modules with the original YOLOv11 backbone layers to improve feature extraction efficiency. Meanwhile, the original C2PSA module is replaced with a CSPCA module incorporating Coordinate Attention, thereby strengthening the model’s spatial awareness and target localization capabilities. Secondly, to improve the detection accuracy of small insulator defects in complex scenes and reduce redundant feature information, a DC-PUFPN neck network is constructed. This network combines deformable convolutions with a progressive upsampling feature pyramid structure to optimize the Neck part of YOLOv11, enabling efficient feature fusion and information transfer, while retaining the original C3K2 module. Additionally, a composite loss function combining Wise-IoUv3 and Focal Loss is adopted to further accelerate model convergence and improve detection accuracy. Finally, the effectiveness and advancement of the proposed DGNCA-Net algorithm in insulator defect detection tasks are comprehensively validated through ablation studies, comparative experiments, and visualization results.