Research on PCB Defect Detection Algorithm Based on Improved YOLOv5

What is it about?

As an essential component of industrial production equipment, PCBs with defects can seriously affect their performance in industrial production. This article improves the YOLOv5 algorithm to improve the detection accuracy of PCB defects.

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Why is it important?

Manual testing requires a large amount of labor and is not efficient. There are alternative methods for manual defect detection, such as defect detection based on electrical characteristics and deep learning. However, the former may have the possibility of secondary damage when using electrical equipment for detection. The main goal of defect detection based on deep learning is to achieve Automated Optical Inspection (AOI) , which requires deep learning networks to accurately and quickly identify targets from images.

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