A light-weight defect detection model for capacitor appearance based on the Yolov5

Abstract

As one of the most important electronic components, capacitors are very important for appearance inspection in the production process. However, the current production process mainly relies on manual inspection, which not only reduces product quality and production efficiency but also increases production costs. The automated detection is limited by the computational resources of the equipment, which is difficult to apply in practice. Therefore, in this paper, we propose a lightweight method for capacitor appearance inspection. We use the YOLOv5 (You Only Look Once Version 5) framework, MobileNet as the backbone network, and GSConv (Ghost convolution) and GSCSP module as the neck depth compression network model to reduce the computational cost. In addition, we incorporate the CBAM (Convolutional Block Attention Module) into the backbone network to improve the network's ability to extract features. For the problem of difficult detection of small targets, we use CIoU (Complete Intersection Union) and NWD (Normalised Gaussian Wasserstein Distance) to design a new loss function. By testing our method on capacitor appearance defect data, compared to the baseline, the model computational cost FLOPs was reduced by 130 %, the model size was reduced by 94%, the accuracy reached 92.5%, and the mAP (mean average precision) reached 92.3%, while the number of frames detected per second was up to 58 frames. The experimental results show that our method is capable of real-time detection of capacitor appearance defects, providing strong theoretical support for practical applications.