基于遮挡环境下的奶牛腿部跟踪与跛行智能检测方法  

Leg tracking and lameness intelligent detection method for dairy cows based on occlusion environment

作　　者：黄筠竹 李亚萍[1] 张康 张金硕 李春娇 蒙贺伟[1] HUANG Junzhu;LI Yaping;ZHANG Kang;ZHANG Jinshuo;LI Chunjiao;MENG Hewei(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832003

出　　处：《农业工程学报》2024年第24期179-189,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：新疆农机研发制造推广应用一体化试点项目(YTHSD2022-19-3);江苏大学现代农业装备与技术教育部重点实验室和江苏省农业装备与智能化高技术研究重点实验室开放课题(MAET202309)。

摘　　要：奶牛跛行行为信息间接反映其腿部的健康状态,准确跟踪奶牛腿部是实现跛行检测的重要基础。然而现有奶牛跛行智能检测常设于养殖场挤奶通道处,此视角下奶牛腿部存在遮挡、模糊、形变问题,该研究针对此类问题提出了一种基于遮挡环境下的奶牛腿部跟踪与跛行智能检测方法。首先,设计了改进FairMOT的奶牛腿部目标跟踪模型,获取奶牛腿部轨迹信息;其次,基于奶牛步态周期变化规律,设计了基于遮挡环境下的奶牛运动轨迹曲线拟合方法;最后通过个体阈值进行奶牛步态运动的阶段划分和参数获取,实现对跛行行为的检测。试验结果表明,改进模型具有良好的定位精度和跟踪效果,相比FairMOT原始模型,多目标跟踪准确度(MOTA,multiple object tracking accuracy)提升3.79个百分点,多目标跟踪精确度(MOTP,multiple object tracking percision)和目标ID切换总数(IDs,the total number of identity switches)分别降低0.022,35;跛行检测的分类准确率为86.67%,该研究结果为后续奶牛跛行行为的智能监测提供参考。Livestock farming is ever increasing in production scale and automation in recent years.Accurate and efficient monitoring of individual livestock can greatly contribute to the growth and health status of animals.Behavioral and health information can often be involved in the individual livestock.Among them,the lameness behavior of a cow can indirectly represent the health status of its legs.Accurate leg tracking is very critical to detect the lameness of a cow.However,the existing intelligent detection of cow lameness is often used at the milking aisle of the farm.There are the occlusion,blurring,and deformation of the leg in this viewpoint.In this study,leg tracking and lameness detection were proposed for dairy cows in the occlusion environment.Firstly,the video of cows walking was recorded and then pre-processed in an actual farming setting.50 video segments were chosen as the samples for the multi-target tracking dataset of cow legs.Each segment was featured as a walking cow with a duration of 8-20 s.The dataset included a total of 27 healthy and 23 lame cows.In the dataset samples,the training and testing sets were randomly divided in the ratio of 8:2.40 videos for model training,and 10 videos were used for model testing.Secondly,the improved FairMOT target tracking model of the cow leg was designed to obtain the cow leg trajectory.Specifically,the coordinate attention(CA)module and Squeeze-Excitation attention(SE)module were introduced into the FairMOT Iterative Deep Aggregation(IDA)and Hierarchical Deep Aggregation(HDA),respectively.The important features of the cow's leg were focused on reducing the interference of redundant information under shape mutation and blurring along the spatial and channel directions.NSA Kalman filtering was introduced into the trajectory state for the updating phase,in order to obtain more accurate motion states of leg targets.Tracking performance was improved to reduce the measurement weights of low-confidence detection targets when the leg occlusion occurred;the AFLink linking

关 键 词：机器视觉 模型 奶牛 腿部跟踪 跛行检测 FairMOT 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]