PCB Defect Detection Based on Image Processing and Improved YOLOv5

Abstract

Aiming at the problems of the high cost of PCB circuit board defect detection, many types of defects, and complex shapes, this paper proposes a PCB defect detection method that combines image processing and deep learning. We use the pruned YOLOv5 algorithm and image processing method to realize the identification and location of defects on the PCB board, which improves the accuracy of defect detection. The combination algorithm first puts the original image through image enhancement methods such as straightening, denoising, sharpening, and contrast enhancement, and uses the improved YOLOv5 convolutional neural network to identify defects. The experimental results show that, without reducing the detection accuracy of the original model, the algorithm compresses the model to 2.64 MB and shortens the inference time to 20.53 ms, which greatly improves the model deployment efficiency and detection speed.