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.
wafer localizationobject detectionattention mechanismlightweight networkloss function
万方数据
维普
