Abstract: Steel surface defect detection technology in industrial scenarios has problems such as low detection accuracy and slow convergence speed, To address these issues, an improved YOLOv8 algorithm (YOLOv8-Steel surface defect detection method based on Multi-scale Cross-fertilization Network,Deformable Convolution and Coordinate Attention,YOLOv8-MDC). Firstly, the MCN feature pyramid is added into the backbone network to fully fuse the semantic information between different features; secondly, deformable convolution is introduced into the module to adaptively change the shape and position of the convolution kernel (Deformable Convolution-C2f, DCN-C2f); finally, the CA attention mechanism is added to embed the positional information into the channel attention, which solves the problem of location information loss. The experimental results on the NEU-DET dataset show that the YOLOv8n-MDC algorithm on the NEU-DET dataset has an average accuracy value (mAP@IoU=0.5) of 81.0%, which is 4.2% higher than that of the original baseline network, and the algorithm has a faster convergence speed and higher accuracy, which is better able to meet the requirements of practical industrial production.

摘要： 在工业场景下钢材表面缺陷检测技术存在检测精度低、收敛速度慢等问题，为解决这些问题，提出了一种改进的YOLOv8算法（YOLOv8-Steel surface defect detection method based on Multi-scale Cross-fertilization Network,Deformable Convolution and Coordinate Attention,YOLOv8-MDC）。首先，在骨干网络中加入多尺度交叉融合网络，充分融合不同特征之间的语义信息；其次在模块中引入可变形卷积，自适应地改变卷积核的形状与位置（Deformable Convolution-C2f，DCN-C2f）；最后，加入CA注意力机制，将位置信息嵌入到通道关注中，解决了位置信息丢失的问题。在NEU-DET数据集上的实验结果表明，YOLOv8n-MDC算法在NEU-DET数据集上的平均精度值（mAP@IoU=0.5）达到了81.0%，相比与原基准网络提升了4.2个百分点，该算法收敛速度较快，精度较高，更能满足实际工业生产的要求。