Abstract: A steel surface defect detection method based on comprehensive attention was proposed to improve the detection performance of steel surface defects for problems such as low-contrast between defects and background， large differences in the multiple scales of the intra⁃class defects. 1） Feature extraction was performed based on the convolution and self-attention hybrid modules to obtain feature maps with local detail feature information and long⁃distance pixel dependencies， which helps to enhance the processing ability for changes in shape and size of intra⁃class features， and to improve the robustness of complex background detection. 2） A comprehensive attention structure was proposed， which included a spatial attention module， a channel attention module and a self-attention module. The attention mechanism was fully used to extract the features of current feature maps， highlight defect objects in steel surface images with background noise. The experimental results showed that the performance of the proposed method on the NEU⁃DET and GC10⁃DET datasets were improved， which verified the effectiveness and generalization ability of the method.