Strip steel surface defect detection has become one of the important links to guarantee the quality of strip steel production. Aiming at the problem of improving the detection accuracy of current strip steel defect detection algorithm, an improved MT-YOLOv5 algorithm based on YOLOv5 is proposed. Firstly, introducing Transformer self-attention mechanism in the backbone network to make the network more focused on the extraction of global image feature information. Secondly, combining the Transformer layer with the BiFPN structure, and the T-BiFPN network is used to further enhance the fusion of image shallow feature information and deep feature information. Then, an improved lightweight network RepVGG is introduced to replace part of the convolutional layers in the backbone network, which can enhance the feature extraction capability of backbone network. Finally, adding a prediction layer to detect objects of different scales. The experimental results show that the value of mean average precision (mAP) of the MT-YOLOv5 algorithm is 82. 4% on the NEU-DET dataset, which is 5. 3% higher than the original YOLOv5s algorithm, and the detection speed reaches 65. 4 fps, which achieves a better balance between detection speed and detection accuracy.