PCB Defect Detection Based on Evolutionary Object Detection Algorithm

In PCB defect detection, traditional methods often face challenges such as high manual labor costs and slow processing speeds. This paper introduces an improved approach using the YOLOv5 model, which combines the latest developments in target detection and deep learning. The YOLOv5 model stands out by providing a simpler implementation framework, improved detection performance, and accelerated processing capabilities, especially excelling in detecting small-scale targets. The paper extensively discusses the PCB manufacturing process, emphasizing the importance of reliable component encapsulation and external circuit connectivity. Furthermore, it conducts a comprehensive analysis of the YOLOv5 model structure, highlighting its operational simplicity, compact model size, and clear structural layout, which have played a crucial role in its widespread adoption in industrial applications. Finally, through extensive experimental analysis, the paper validates that the optimized YOLOv5 model achieves an average precision mean of 98.2% in PCB defect detection, a 0.6% improvement compared to the original model. These results demonstrate that the improved YOLOv5 model is an effective solution for PCB defect detection, laying the foundation for its practical implementation in modern manufacturing processes.