基于YOLOv5改进算法的印花图案疵点检测

针对圆网印花图案疵点检测问题，本文采用了一种基于YOLOv5改进算法模型来检测印花图案的疵点。本实验根据实际的情况对YOLOv5模型网络结构进行了更改，首先，对YOLOv5网络的骨干部分进行优化改进，引入了注意力机制模块，对输入图片的通道注意力和空间注意分别提取特征。其次，针对印花疵点目标较小的情况对网络的检测层结构进行了修改。实验结果显示，改进的YOLOv5检测算法精确率提升了14.4%，检测速度提升了7.6fps，达到了43.1fps满足实时检测要求。

Printing pattern defect detection based on improved yolov5 algorithm

Aiming at the problem of defect detection of circular screen printing pattern, an improved algorithm model based on yolov5 is used to detect the defect of printing pattern. In this experiment, the network structure of yolov5 model is changed according to the actual situation. Firstly, the backbone of yolov5 network is optimized and improved, and the attention mechanism module is introduced to extract the features of channel attention and spatial attention of input pictures respectively. Secondly, aiming at the small target of printing defects, the detection layer structure of the network is modified. The experimental results show that the accuracy of the improved yolov5 detection algorithm is improved by 14.4%, and the detection speed is also improved, reaching 43.1fps, which meets the requirements of real-time detection.