Scale of steel surface defects is different, but existing detection algorithms have poor multi-scale feature processing ability and low accuracy. Therefore, an improved YOLOv5 algorithm for steel surface defect detection is proposed. First, receptive field modules are added after the feature output layer of the backbone to enhance the discrimination and robustness of the features which can better perceive the feature information of different scales. Then, aligned feature aggregation modules are used to replace the traditional feature fusion structure to solve the feature misalignment problem in the fusion process of high and low resolution feature maps. Finally, decoupled heads with efficient channel attention mechanisms are used to output the detection results. The attention mechanism can adaptively calibrate the channel response, and the decoupled heads enable classification and regression tasks to be performed independently. The experimental results on NEU-DET dataset show that the mean average precision of the proposed method is 80.51%, which is 4.48% higher than that of the benchmark model, and the detection speed is 31.96 frame/s. Compared with other mainstream object detection algorithms, the proposed algorithm has higher accuracy while maintaining certain detection speed, enabling efficient steel surface defect detection.