In order to solve the problem that traditional wafer positioning is easily affected by lightness,noise and other fac-tors and consume a lot of resources,the wafer detection algorithm YOLOv5s-wafer was constructed.The wafer detection dataset was constructed firstly,the lightweight network GhostNetv2 was used as the backbone feature extraction network to reduce the amount of model parameters.Then the CA attention mechanism was introduced into the feature fusion network to enhance feature extraction capabilities.Finally,EIOU was used as positioning loss function to improve the wafer detection accuracy.The experi-mental results show that the detection algorithm average accuracy is 99.3%,the parameter number is 4.637×106,which achieves an ideal balance between the detection performance and the lightweight of the algorithm.
关键词
晶粒定位/目标检测/注意力机制/轻量级网络/损失函数
Key words
wafer localization/object detection/attention mechanism/lightweight network/loss function
维普
万方数据