Li Zhongkun, Cheng Guodong, Yang Lu, Han Shuqing, Wang Yali, Dai Xiaofei, Fang Jianyu, Wu Jianzhai
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Animals (Basel). 2025 Aug 20;15(16):2439. doi: 10.3390/ani15162439.
With the development of precision livestock farming, in order to achieve the goal of fine management and improve the health and welfare of dairy cows, research on dairy cow motion monitoring has become particularly important. In this study, considering the problems surrounding a large amount of model parameters, the poor accuracy of multi-target tracking, and the nonlinear motion of dairy cows in dairy farming scenes, a lightweight detection model based on improved YOLO v11n was proposed and four tracking algorithms were compared. Firstly, the Ghost module was used to replace the standard convolutions in the YOLO v11n network and a more lightweight attention mechanism called ELA was replaced, which reduced the number of model parameters by 18.59%. Then, a loss function called SDIoU was used to solve the influence of different cow target sizes. With the above improvements, the improved model achieved an increase of 2.0 percentage points and 2.3 percentage points in mAP@75 and mAP@50-95, respectively. Secondly, the performance of four tracking algorithms, including ByteTrack, BoT-SORT, OC-SORT, and BoostTrack, was systematically compared. The results show that 97.02% MOTA and 89.81% HOTA could be achieved when combined with the OC-SORT tracking algorithm. Considering the demand of equipment in lightweight models, the improved object detection model in this paper reduces the number of model parameters while offering better performance. The OC-SORT tracking algorithm enables the tracking and localization of cows through video surveillance alone, creating the necessary conditions for the continuous monitoring of cows.
随着精准畜牧养殖的发展，为实现精细化管理目标并改善奶牛的健康和福利，奶牛运动监测研究变得尤为重要。在本研究中，考虑到大量模型参数、多目标跟踪精度差以及奶牛在养殖场景中的非线性运动等问题，提出了一种基于改进的YOLO v11n的轻量级检测模型，并比较了四种跟踪算法。首先，使用Ghost模块替换YOLO v11n网络中的标准卷积，并替换了一种名为ELA的更轻量级的注意力机制，这使得模型参数数量减少了18.59%。然后，使用一种名为SDIoU的损失函数来解决不同奶牛目标大小的影响。通过上述改进，改进后的模型在mAP@75和mAP@50 - 95上分别提高了2.0个百分点和2.3个百分点。其次，系统地比较了包括ByteTrack、BoT - SORT、OC - SORT和BoostTrack在内的四种跟踪算法的性能。结果表明，与OC - SORT跟踪算法结合时，可以实现97.02%的MOTA和89.81%的HOTA。考虑到轻量级模型中设备的需求，本文改进后的目标检测模型在减少模型参数数量的同时提供了更好的性能。OC - SORT跟踪算法能够仅通过视频监控实现奶牛的跟踪和定位，为奶牛的持续监测创造了必要条件。
