To address the high cost and low accuracy of manual inspection for steel surface defects, as well as the excessive computational resource requirements caused by complex traditional target detection models, a lightweight defect detection algorithm named YOLOv8n-MDC was proposed by integrating MobileNetv3 with YOLOv8. Firstly, based on YOLOv8n, the original intersection over union(IoU)-based bounding box loss function was replaced with weighted IoU(WIoU), enhancing model robustness through a non-monotonic focusing mechanism. Secondly, the backbone feature extraction network of YOLOv8n was substituted with MobileNetv3, utilizing its lightweight architecture to reduce network complexity and redundant computational overhead. Finally, during the feature fusion stage, depthwise separable convolution (DWConv) and C3Ghost modules replaced the original components, further minimizing model parameters and accelerating detection speed. Evaluated on the NEU-DET steel surface defect dataset, the YOLOv8n-MDC achieves an mAP of 81.3%, representing a 5% improvement over the baseline YOLOv8n, while its parameter count and computational complexity are reduced to 1.02 M and 2.1 GFLOPs (33.9% and 25.9% of the original model, respectively), meeting industrial requirements. This lightweight algorithm significantly reduces computational complexity and resource consumption while enhancing detection accuracy, offering an optimized solution for industrial steel surface defect inspection.