Strip steel surface defect detection algorithm based on improved Faster R-CNN

To solve the problems of the low accuracy and poor real-time performance of traditional strip steel surface defect detection methods, which are caused by the characteristics of many kinds, complex shapes, and different scales of strip surface defects, a strip steel surface defect detection algorithm based on improved Faster R-CNN is proposed. Firstly, the residual convolution module is inserted into the Swin Transformer network module to form the RC-Swin Transformer network module, and the RC-Swin Transformer module is introduced into the backbone network of the traditional Faster R-CNN to enhance the ability of the network to extract the global feature information of the image and adapt to the complex shape of the strip steel surface defect. To improve the attention of the network to defects in the image, a CBAM-BiFPN network module is designed, and then the backbone network is combined with the CBAM-BiFPN network to realize the detection and fusion of multi-scale features. The RoI align layer is used instead of the RoI pooling layer to improve the accuracy of defect location. Finally, Soft NMS is used to achieve non-maximum suppression and remove redundant boxes. In the comparative experiment on the NEU-DET dataset, the improved algorithm improves the mean average precision by 4.2% compared with the Faster R-CNN algorithm, and also improves the average precision by 6.1% and 6.7% for crazing defect and rolled-in scale defect, which are difficult to detect with the Faster R-CNN algorithm. The experiments show that the improvements proposed in the paper effectively improve the detection accuracy of the algorithm and have certain practical value.