Steel Surface Defects Detection Based on Improved Faster R-CNN

Steel surface defects Detection is crucial to improving the quality of steel production. However, the high-speed production lines, defect diversification, and tiny defects make the detection of steel surface defects difficult. This paper presents a steel surface defects detection model based on an improved Faster R-CNN. Firstly, to improve the generalization of the model, the ResNet50 network is replaced by the RegNet network. Then the transformer spatial attention is utilized to make the network focus more on the targets. Finally, transfer learning, multi-scale training, and cosine annealing learning rate are used to further improve the detection accuracy. Compared with the other nine models, the proposed model has superior performance in the simulation results. The improved model can effectively improve the accuracy of steel surface defects detection.