基于改进YOLOv8的梳棉机棉网上棉结检测方法  

Image detection of cotton nep in carding net based on improved YOLOv8

作　　者：白雨薇 徐健[1] 朱耀麟[1] 丁展博 刘晨雨 BAI Yuwei;XU Jian;ZHU Yaolin;DING Zhanbo;LIU Chenyu(School of Electronics and Information,Xi′an Polytechnic University,Xi′an,Shaanxi 710048,China)

机构地区：[1]西安工程大学电子信息学院,陕西西安710048

出　　处：《纺织学报》2025年第3期56-63,共8页Journal of Textile Research

基　　金：陕西省自然科学基金重点项目(2023-JC-ZD-33)。

摘　　要：针对基于深度学习的棉结目标检测模型占用过多计算资源、难以满足嵌入式设备及移动端的实时在线检测的问题,提出基于改进型YOLOv8的梳棉机棉网上棉结检测方法。首先,将轻量型网络MobileNetv3_Small用作YOLOv8n骨干网络,降低计算参数量;其次,在MobileNetv3网络中使用自改进协调注意力机制(coordinate attention)模块替换原有的压缩和激励(squeeze-and-excitation)注意力机制模块,提升对棉结的检测精度;最后,使用EIoU损失函数取代原YOLOv8n中的CIoU损失函数,在处理数据时保留更多有效信息。在自制棉结图像数据集上验证改进型YOLOv8算法的检测效果,结果表明:基于改进型YOLOv8的检测方法平均准确率均值达到95.8%,相较于改进前提升了2.6%;参数量减少了34.2%。改进后算法的检测效果更好,且模型更加轻量,可满足嵌入式设备的使用。Objective In order to improve the nep detection using deep learning with minimal computing resources and to achieve real-time online detection using embedded devices and mobile terminals,this research proposed an improved YOLOv8 detection method.Method Lightweight network MobileNetv3_Small was used as YOLOv8 backbone network so as to reduce the number of parameters.The self-improved CA(coordinate attention)model was adopted to replace the SE(squeeze-and-excitation)attention mechanism model in MobileNetv3 so as to improve the accuracy of nep detection.EIoU loss function substituted CIoU loss function was employed to retain more effective information in data processing.Results The dataset constructed includes three target objects,which were named lumpy nep,strip nep and cotton miscellaneous matters.Different light intensities and camera orientation on model detection effec was studied by using test set.The experimental results show that no missed detections and false detections of all target objects was found under different light intensities and different camera orientations.The average accuracy of improved YOLOv8 model reached 95.8%,with an increase of 2.6%compared with the reported model.Compared with the improved YOLOv8,YOLOv7,YOLOv5 and Faster R-CNN network models,the improved YOLOv8 network model involved fewer parameters but with higher average accuracy.According to the average accuracy of the improved YOLOv8 model for each type,the detection rate reached more than 95.1%with high accuracy.The improved model ablation experiments show when MobileNetv3 replaced the YOLOv8 backbone network,the parameter quantity is greatly reduced,and it was found from the comparison experiment between the loss function CIoU and EIoU that when the loss function is EIoU,the average accuracy of model detection is higher than CIoU.In addition,the number of parameters in the model became smaller,which is beneficial for embedded equipment and would satisfy the actual industrial production requirements.Conclusion A nep detection metho

关 键 词：梳棉机棉网 深度学习 目标检测 棉结 轻量化模型 YOLOv8 图像检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]