Objective In response to the issues of low detection accuracy missed detections and false alarms in existing steel surface defect detection methods an improved defect detection algorithm YOLOv8-SSDW based on YOLOv8 was proposed. Methods This algorithm took YOLOv8n as the benchmark model and introduced the SKNet Selective Kernel Networks attention module into the backbone network structure to enhance the feature extraction and adaptability of the backbone network allowing the network to pay more attention to defect targets during the feature extraction process. At the same time the Slim-Neck structure was used in the neck of YOLOv8 to reduce the number of model parameters and computational load. To further enhance the network ?? s feature extraction capability a deformable convolution fusion method was proposed to strengthen the feature learning for defect targets. Considering the imbalance in defect sample quality the WIoU wise intersection over union loss function was used which effectively addressed the issue through its gradient gain allocation strategy enhancing model convergence speed and regression accuracy. Results Experiments on the steel dataset showed that the average accuracy of the improved model reached 85. 5% which was an increase of 2. 7% over the benchmark model. Conclusion Extensive experiments demonstrate the effectiveness of the improved network which resolves the issue of low accuracy in steel strip surface defect detection reduces missed and false detections and meets real-time requirements. Compared with current mainstream models the proposed model has certain advantages in detection accuracy and offers a valuable reference for practical detection in future research.