CGS-YOLOv5: a defect detection algorithm for PCB board based on YOLOv5 algorithm

Printed Circuit Board (PCB) is one of the core components of electronic equipment, ensuring the quality and reliability of PCB board is essential for product performance, life and safety. In view of the low detection accuracy and large number of model parameters in the traditional algorithm of PCB, this paper improves YOLOv5 model, using the ghost module instead of the standard convolution layer, to ameliorate the performance, make the model more lightweight. Employ coordinate attention mechanism module, improve the detection of PCB Small goals. Adopt the SIOU loss function, enhance the precision of the model. The empirical findings confirm that the improved YOLOv5 improves the average accuracy by 1.4% over a 47% reduction in parameter size.