Objective Surface defects in steel remain a critical challenge for industrial production and safety. To address the problems of low accuracy false detection and missed detection in steel surface defect detection a steel surface defect detection algorithm based on multi-scale features named GPDN-Yolov5 is proposed. Methods First to tackle the problem of certain defects blending with the background an improved coordinate attention mechanism GJ-CA was incorporated into the backbone network to enhance focus on defect features. Second considering the wide variation in defect sizes a multi-scale convolutional block PKI Block was introduced to strengthen the network?? s ability to extract multi-scale features. Then a Dynamic head was applied as the detection head of the improved algorithm which strengthened the expression ability of the detection head and the detection ability of multi-scale features. Finally the CIOU loss function was replaced by a weighted combination of the normalized Wasserstein distance NWD loss function and the WIOU loss function thereby addressing the original model?? s bias toward easy samples and its high sensitivity to positional deviation in low-pixel defects. Results The GPDN-Yolov5 algorithm achieves a mean average precision mAP of 80. 5% on the NEU-DET dataset representing a 6. 9% improvement over the baseline Yolov5 algorithm and outperforms other classical detection models. Moreover on the GC10-DET dataset the algorithm improves the mean average precision by 4. 7% over Yolov5 demonstrating its consistent performance gain across different datasets. Conclusion The GPDN-Yolov5 algorithm provides a significant improvement in detection accuracy and holds certain value for steel defect detection.