Abstract: In the field of industrial quality inspection, there are common problems in the detection of steel surface defects, such as insufficient fusion of target features, missed detection of fine edge defects, and unbalanced sample classification. Therefore, a steel surface defect detection algorithm based on multi-scale interaction and dynamic collaboration is proposed. In the backbone network, by fusing the shifted sparse convolution and inverted residual structure, the interactive fusion of defect features under different receptive fields is strengthened, and the feature expression ability of multi-scale defects is improved. Introduce the large separation kernel attention mechanism to dynamically enhance the feature response to fine defect areas and reduce the missed detection rate of cracks and inclusions. In the neck network, by combining the DySample dynamic upsampling strategy, dynamic upsampling based on defect content is achieved, which not only improves the clarity of the defect contour of small targets but also reduces computational redundancy, adapting to the deployment of edge devices. In addition, an EMASlideLoss loss function integrating exponential moving average and sliding threshold mechanisms is designed to dynamically balance the learning weights of difficult and easy samples, thereby improving the detection deviation caused by the uneven distribution of defect samples. Experiments on the NEU-DET dataset show that the mean mAP50% of the average accuracy of this algorithm reaches 84.4%, which is 5.8% higher than that of the original YOLO11n. While the precision and recall rates increase by 5.2% and 4.8% respectively, the computational load decreases by 8%. This algorithm not only optimizes the computational efficiency but also improves the detection accuracy, and is more capable of meeting the detection requirements in industrial scenarios.

摘要： 业质检领域，钢表面缺陷检测普遍存在目标特征融合不足、边缘细微缺陷漏检及样本分类不均衡等问题，为此提出一种多尺度交互与动态协同的钢表面缺陷检测算法。在主干网络中，通过融合移位稀疏卷积和倒置残差结构，强化不同感受野下缺陷特征的交互融合，提升多尺度缺陷的特征表达能力；引入大分离核注意力机制，动态增强对细微缺陷区域的特征响应，降低裂纹、夹杂的漏检率；在颈部网络中，结合DySample动态上采样策略，实现基于缺陷内容的动态上采样，在提高小目标缺陷轮廓清晰度的同时减少计算冗余，适配边缘设备部署；此外，设计融合指数移动平均与滑动阈值机制的EMASlideLoss损失函数，动态平衡难易样本的学习权重，改善缺陷样本分布不均导致的检测偏差。在NEU-DET数据集上的实验表明，该算法平均精度均值mAP50%达到84.4%，相比于原始YOLO11n提升5.8%，精确率和召回率分别提升5.2%、4.8%的同时计算量下降8%。该算法在优化计算效率的同时提高了检测精度，更能满足工业场景下的检测需求。