机械与电子2023，Vol.41Issue(12)：20-26,7.
基于深度学习的纱管识别方法研究
Research on Yarn Bobbin Detection Method Based on Deep Learning
摘要
Abstract
To improve the efficiency of automatic winding machines,it is particularly important to im-prove the accuracy of visual inspection.This paper proposes an improved yarn bobbin detection method based on YOLOv5.The original SiLU activation function of the network is replaced by the Mish activation function with better expressive power.The CIoU localization loss function is replaced with the SIoU loss function,which takes into account the directional matching between the true and predicted Bounding Box,allows the network to converge more quickly.The original C3 module is replaced with a CCA module em-bedded with a CA attention mechanism,which gives the network better performance in extracting features.Besides,the yarn bobbin dataset is produced and data augmentation is applied for better robustness and generalization of the model.Through the experiment,it was concluded that the improved YOLOv5 network proposed in this paper achieved 97.30％in recognition precision;98.17％in recall;and 98.58％in mAP_0.5.The improved network has a significant improvement in recognition performance compared with the o-riginal network.关键词
纱管识别/注意力机制/深度学习/YOLOv5Key words
yarn bobbin detection/attention mechanism/deep learning/YOLOv5分类
信息技术与安全科学引用本文复制引用
王青,吕绪山,党帅,姜越夫,梁高翔,赵恬恬,薛博文..基于深度学习的纱管识别方法研究[J].机械与电子,2023,41(12):20-26,7.基金项目
陕西省重点研发计划项目(2022GY-307) （2022GY-307）
