Improved YOLOv7 Wafer Character Detection Algorithm
For character detection in wafer processing,an improved object detection model based on YOLOv7.A Swin Transformer module is inserted before the SPP layer of the original YOLOv7 to enhance the network's ability to obtain global information and improve the integration ability of global and local features.Then the A2-Net attention mechanism is inserted in the prediction part,and the feature information is reallocated after global fusion to improve the robustness of the network.Finally,the SIOU Loss function is used to replace CIOU in the location loss function.The introduction of angle loss increases the accuracy of character location detection.On the self-made character dataset,the experimental results show that compared with the traditional model,the improved model improves the mAP by 5.02％,and the FPS is higher than that of the traditional algorithm.It also achieves good results in practical use.
wafer character detectionYOLOv7 networkSwin Transformer moduleattention moduleloss function
万方数据
维普
