改进YOLOv5的钢材表面缺陷检测算法

针对传统钢材表面缺陷检测方法存在检测效率低、检测精度差等问题，提出一种基于改进YOLOv5的钢材表面缺陷检测算法。首先使用GhostBottleneck结构替换原YOLOv5网络中的C3模块和部分卷积结构，实现网络模型轻量化；其次在Backbone部分引入SE注意力机制，对重要的特征通道进行强化；最后针对数据集特点在网络中增加一个检测层，强化特征提取能力，并在Neck部分增加特征融合结构，使用DW卷积替换部分标准卷积以减少运算量。实验表明，改进的YOLOv5sGSD算法，模型体积减少了10.4%，在测试集上的mAP值为76.8%，相比原YOLOv5s网络提高了3.3%，检测精度和速度也明显高于一些主流算法。相比传统的钢材表面缺陷检测方法，提出的算法能够更加准确、快速地检测出钢材表面缺陷的种类和位置，并且具有较小的模型体积，方便于在移动端的部署。

Steel Surface Defect Detection Algorithm Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor accuracy of traditional steel surface defect detection methods, a steel surface defect detection algorithm based on improved YOLOv5 is proposed in this paper. Firstly, the C3 module and part of convolutional structure in YOLOv5 network are replaced by GhostBottleneck structure to realize the lightweight of network model. Secondly, SE attention mechanism is introduced in Backbone to strengthen the important feature channels. Finally, according to the characteristics of the data set, a detection layer is added to the network to strengthen the feature extraction ability, and a feature fusion structure is added in the Neck part. DW convolution is used to replace part of the standard convolution to reduce the computation. Experimental results show that the improved Yolov5s GSD algorithm reduces the model volume by 10.4%, and the mAP value on the test set is 76.8%. Compared with the original YOLOv5s network, the detection accuracy and speed are obviously higher than some mainstream algorithms. Compared with traditional steel surface defect detection methods, the algorithm proposed in this paper can detect the type and location of steel surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals.