Abstract: Steel surface defect detection is becoming increasingly important in industrial product quality control. Due to the complex background, variety of defects and different scales of steel surface defects, accurate and efficient detection of strip surface defects is still a challenging task. In order to solve these problems, a strip surface defect salient object detection model based on channel and spatial attention was proposed. Firstly, based on Transformer, the multiscale features of strip steel images were extracted to capture the long-distance dependence of the target image; Then, the acquired feature map was fed into two different attention modules: The channel attention module and the spatial attention module, in order to emphasize the characteristics of the strip steel surface defect and suppress irrelevant background features, so as to strengthen the ability to distinguish between the defect target and the background; Finally, the multiscale progressive fusion module was used to integrate the multiscale feature map, so that the feature information of different scales could be complementary, obtaining feature maps with rich semantic information which enables the model to detect strip surface defects highly efficient and accurately. A large number of experimental results showed that the proposed model had great advantages in the salient object detection task with higher accuracy and stronger robustness.