Insulator detection and damage identification based on improved lightweight YOLOv4 network

Abstract

Aiming at the problem that the detection and damage recognition algorithm of insulator aerial images is difficult to be applied to the embedded platform, this paper proposed an improved Tiny-YOLOv4 lightweight target detection network algorithm. This algorithm combined the Self-Attention mechanism and ECA-Net (Efficient Channel Attention Neural Networks), which could greatly reduce the complexity of the original YOLOv4 algorithm, and the model size is 24.9 MB. Under the condition of ensuring the detection accuracy (>91%), the detection speed is as high as 94FPS. It is transplanted to the Jetson Xavier NX embedded platform, and the average detection speed reaches 22FPS, which effectively meets the real-time detection requirements in the power inspection.