In order to solve the problems of low detection accuracy and large model size in steel surface defect detection,an improved PC-YOLOv7 algorithm model based on YOLOv7-tiny network is proposed.First,the PC-ELAN structure is used to replace part of the ELAN structure in the backbone network to reduce the number of model parameters and model size.Secondly,in the Neck part,bidirectional feature pyramid network(BiFPN)is used to enhance the fusion performance of high-level semantic information and low-level feature information of the image.The SPD-Conv layer is introduced at the output to improve the model's ability to detect low-resolution objects.Finally,the SimCS-CA module is proposed and the feature fusion network is introduced to enhance the model's feature representation performance.Experimental results show that the mean average precision(mAP)of the PC-YOLOv7 algorithm on the NEU-DET dataset is 78.5％.Compared with the original YOLOv7-tiny algorithm,the accuracy and mAP are improved by 10.6％and 4.2％respectively when the model size is reduced,which verifies the effectiveness of the improved algorithm.
NETL
NSTL
维普
万方数据
