棉纺织技术2023，Vol.51Issue(12)：5-11,7.
基于改进YOLOv7的织物疵点检测算法
Fabric defect detection algorithm based on improved YOLOv7
摘要
Abstract
To solve the problem of low detection accuracy caused by small size of fabric defects and irregular shape,an improved algorithm based on YOLOv7 was proposed.Firstly,a new aggregation network DR-SPD was designed,which combined the dynamic perception ability of DRes with the detail extraction ability of SPD to reduce the loss of fine-grained information while maintaining a dynamic regional view.Aiming at the influence of fabric background on detection effect,the GAM attention mechanism was introduced to enhance the model′s anti-interference ability,enabling it to focus on more critical semantic information.Finally,three lateral jump paths were added to the feature fusion network to shorten the distance of information transmission between deep and light layers,reduce detail omissions of detail feature.Experiments showed that mAP value of the improved model was reached 95.63%,the detection speed was 51 flame/s and the overall performance was better than other mainstream models.To verify its effectiveness in industrial scenarios,the improved model was deployed on workshop equipment for testing.The mAP value was reached 94.85%and the detection speed was reached 43 flame/s.It could meet the actual production needs.关键词
织物疵点/卷积神经网络/注意力机制/YOLOv7/机器视觉Key words
fabric defect/convolutional neural network/attention mechanism/YOLOv7/machine vision分类
轻工业引用本文复制引用
郭殿鹏,柯海森,李孝禄,施庚伟..基于改进YOLOv7的织物疵点检测算法[J].棉纺织技术,2023,51(12):5-11,7.基金项目
浙江省科技计划项目(2023C01163) （2023C01163）
