基于改进YOLO v8的行李追踪技术  

Baggage Tracking Technology Based on Improved YOLO v8

作　　者：曹超 顾幸生[1] CAO Chao;GU Xingsheng(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《计算机工程与应用》2024年第9期151-158,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(61973120)。

摘　　要：在机场行李分拣场景下,传统多目标追踪算法存在目标ID切换率高和目标轨迹误报率高的问题。提出一种基于改进YOLO v8和ByteTrack算法的行李追踪技术。增加了CBAM模块,替换ADH解耦头以及改变训练时的损失函数,增加了检测精度,加强了目标特征的判别性,降低目标的ID切换率。在Byte数据关联中进行了GSI插值后处理,不仅利用了高分框和低分框,也使得长时间遮挡后的追踪效果得到保证,降低了因遮挡产生的ID错误切换。在机场行李分拣数据集上,MOTA和IDF1分别达到了89.9%和90.3%,有了较为明显的提升,能稳定地实现对行李箱ID的追踪。In the airport baggage sorting scenario,the traditional multi-target tracking algorithm has the problems of high target ID switching rate and high false alarm rate of target trajectory.This paper presents a baggage tracking technique based on improved YOLO v8 and ByteTrack algorithms.The CBATM module is added,the ADH decoupling head is replaced and the loss function during training is changed,the detection accuracy is increased,the discrimination of target features is strengthened,and the ID switching rate of the target is reduced.GSI interpolation processing in Byte data asso�ciation,which not only uses high box and low box,but also ensures the tracking effect after a long time of occlusion,and reduces the ID error switching caused by occlusion.In the airport baggage sorting dataset,MOTA and IDF 1 reach 89.9%and 90.3%,respectively,which show a significant improvement and can steadily realize the tracking of luggage ID.

关 键 词：机场行李分拣 多目标跟踪 基于检测的跟踪 YOLO v8 ByteTrack 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]