Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation

Abstract

Insulators are essential for electrical insulation and structural support in transmission lines. With the advancement of deep learning, object detection algorithms have become primary tools for detecting insulator defects. However, challenges such as low detection accuracy for small targets, weak feature map representation, the insufficient extraction of key information, and a lack of comprehensive datasets persist. This paper introduces OD (Omni-dimensional dynamic)-YOLOV7-tiny, an enhanced insulator defect detection method. We replace the YOLOv7-tiny backbone with FasterNet and optimize the convolution structure using PConv, improving spatial feature extraction efficiency and operational speed. Additionally, we incorporate the OD (Omni-dimensional dynamic)-SlimNeck feature fusion module and a decoupled detection head to enhance accuracy. For deployment on edge devices, channel pruning and channel-wise distillation are applied, significantly reducing model parameters while maintaining high accuracy. Experimental results show that the improved model reduces parameters by 53% and increases accuracy and mean average precision (mAP) by 3.9% and 2.2%, respectively. These enhancements confirm the effectiveness of our lightweight model for insulator defect detection on edge devices.

Keywords

• object detection
• YOLOv7-tiny
• insulator defect
• channel pruning
• channel-wise distillation