Objective To address the problems of insufficient detection accuracy and high model complexity in existing steel surface defect detection algorithms an improved steel surface defect detection algorithm based on YOLOv8 named YOLOv8-RDP is proposed. Methods First the RepNCSPELAN4 module was introduced to replace the C2f module in the YOLOv8n model. The feature extraction and integration capability of the model was optimized through parallel processing of features at different scales and their fusion in the final convolution layer. Second a deformable attention DA mechanism was integrated into the backbone network. This mechanism adaptively adjusted the sampling points of convolutional kernels strengthening the model?? s ability to capture features of objects with varying shapes and sizes and thereby improving the efficiency of capturing key information. Finally to reduce the computational resources required by the model the detection head of the baseline model was modified using the partial convolution Pconv module. By leveraging redundancy in feature maps and dynamically adjusting the active area of convolutional kernels based on data availability the computational load was reduced. Results Experimental results on the NEU-DET dataset show that YOLOv8-RDP achieved an mAP of 78. 8% representing a 1. 8% improvement over the baseline model. The number of parameters was reduced to 1. 87 M and GFLOPs dropped to 3. 5 G representing decreases of 37. 9% and 57. 0% compared with the baseline model respectively. The improved model significantly reduced the demand for computational resources while maintaining high accuracy. Conclusion The YOLOv8-RDP algorithm demonstrates high detection a th c i c s u f r i a e c l y d.