针对钢材表面缺陷人工检测成本高昂、检测精度不高，以及传统的目标检测方法模型复杂，导致对终端检测设备的计算资源要求较高等问题，融合MobileNetv3轻量化YOLOv8算法提出一种轻量级缺陷检测算法YOLOv8n-MDC。首先，以YOLOv8n为基础，将YOLOv8n的自带IoU（intersection over union）候选框损失函数替换成WIoU（weighted IoU）函数，通过增添非单调聚焦机制，提高模型的鲁棒性。其次，使用MobileNetv3网络替换YOLOv8n的骨干特征提取网络模块，将轻量级网络用于特征提取端降低网络复杂度，减少冗余开销。最后，在特征融合阶段使用DW卷积和C3Ghost模块对原网络的相应模块进行替换，使改进后的网络减少模型参数，进一步提升检测速度。使用钢材表面缺陷数据集NEU-DET进行模型验证，YOLOv8n-MDC模型mAP达81.3%，较YOLOv8n模型提升5%；参数量与计算量分别为1.02 M和2.1 GFLOPs，仅为原模型的33.9%和25.9%，达到工业要求。提出的轻量级算法在保证检测精度提升的同时大大降低了算法的复杂度和计算资源的开销，为钢材表面缺陷检测提供了一个优化思路。

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.