利用深度学习实现羊只多目标跟踪  

Achievement of multi-target tracking of sheep using deep learning

作　　者：赵晓霞 袁洪波[1] 程曼[1] Zhao Xiaoxia;Yuan Hongbo;Cheng Man(College of Mechanical and Electrical Engineering,Hebei Agricultural University,Baoding 071001,China)

机构地区：[1]河北农业大学机电工程学院,河北保定071001

出　　处：《河北农业大学学报》2024年第6期24-31,共8页Journal of Hebei Agricultural University

基　　金：河北省重点研发计划项目(21327402D);河北农业大学精准畜牧学科群项目(1090064).

摘　　要：羊只的运动状态能够反映其健康状况,对养殖场环境下的目标羊只运动轨迹进行追踪是获取其运动状态的关键一步。在特定的情况下,例如羊只分娩前或疾病治疗过程中,对少量的目标进行自动化的跟踪具有实用性。为了提高多目标追踪的准确性,本文提出了一种改进的DeepSORT-R多目标跟踪算法。在目标识别阶段,利用添加了注意力机制的YOLOV5-CBAM网络实现羊只目标的检测;在重识别阶段,采用添加了注意力机制的ResNet50网络实现了羊只身份的识别。本文提出的方法针对测试集的试验结果表明,MOTA、MOTP和IDSW分别达到了76.15%,0.208和7.5。此外,在长视频追踪测试过程中,本文提出的方法在评价指标MOTA,MOTP和IDSW的得分上均优于目前常用的YOLOV4+DeepSORT和ByteTrack。试验结果表明本文提出的方法能够用于实际养殖环境中多目标羊只的跟踪。The movement status of the sheep can reflect its health status,and tracking the movement trajectory of the target sheep in the farm environment is a key step to obtain its movement status.Automated tracking of a small number of targets is practical in specific situations,such as before farrowing or during disease treatment.In order to improve the accuracy of multi-target tracking,an improved DeepSORT-R multi-object tracking algorithm was proposed.In the target recognition stage,the YOLOV5-CBAM network with added attention mechanism was used to realize the detection of sheep targets.In the re-identification stage,the ResNet50 network with added attention mechanism was used to realize the identification of sheep identity.The experimental results of the proposed method for the test set showed that MOTA,MOTP and IDSW reached 76.15%,0.208 and 7.5,respectively.In addition,the proposed method was better in the long video tracking test than the commonly used YOLOV4+DeepSORT and ByteTrack in the scores of the evaluation indicators MOTA,MOTP and IDSW.The experimental results showed that the proposed method can be used for the tracking of multi-target sheep in the actual breeding environment.

关 键 词：羊只 多目标追踪 深度学习 注意力机制 YOLO ResNet DeepSORT 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] S826.89[自动化与计算机技术—控制科学与工程]