Optimization of Chip Surface Defect Detection Algorithm Based on YOLOv5
There are many uncertain factors in chip manufacturing,resulting in numerous types of defects and difficulty in defining defect characteristics.Traditional defect detection methods are prone to defects such as missed detections,false detections,and poor accuracy.Therefore,a new integrated circuit surface defect detection algorithm based on YOLOv5 is proposed.Firstly,incorporating attention mechanism into the YOLOv5 network structure to better identify chip surface defects.Then,four improved models were obtained by introducing complex bidirectional fusion networks BiFPN and ASFF.Finally,compare the models to obtain the optimal model for chip surface defect detection.Compared with the original YOLOv5 model,all four new network models show significant improvements.Among them,the mAP obtained a maximum value of 0.723 using CBAM attention mechanism and BiFPN feature fusion network model.
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