Zheng Zhiqiang, Wang Zhuangzhuang, Weng Zhi
College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China.
State Key Laboratory of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot 010021, China.
Animals (Basel). 2024 Dec 19;14(24):3668. doi: 10.3390/ani14243668.
Monitoring the body condition of dairy cows is essential for ensuring their health and productivity, but traditional BCS methods-relying on visual or tactile assessments by skilled personnel-are subjective, labor-intensive, and impractical for large-scale farms. To overcome these limitations, we present BCS-YOLO, a lightweight and automated BCS framework built on YOLOv8, which enables consistent, accurate scoring under complex conditions with minimal computational resources. BCS-YOLO integrates the Star-EMA module and the Star Shared Lightweight Detection Head (SSLDH) to enhance the detection accuracy and reduce model complexity. The Star-EMA module employs multi-scale attention mechanisms that balance spatial and semantic features, optimizing feature representation for cow hindquarters in cluttered farm environments. SSLDH further simplifies the detection head, making BCS-YOLO viable for deployment in resource-limited scenarios. Additionally, channel-based knowledge distillation generates soft probability maps focusing on key body regions, facilitating effective knowledge transfer and enhancing performance. The results on a public cow image dataset show that BCS-YOLO reduces the model size by 33% and improves the mean average precision (mAP) by 9.4%. These advances make BCS-YOLO a robust, non-invasive tool for consistent and accurate BCS in large-scale farming, supporting sustainable livestock management, reducing labor costs, enhancing animal welfare, and boosting productivity.
监测奶牛的身体状况对于确保其健康和生产力至关重要，但传统的体况评分（BCS）方法——依赖熟练人员的视觉或触觉评估——具有主观性、劳动强度大，且对大型农场不实用。为了克服这些限制，我们提出了BCS-YOLO，这是一个基于YOLOv8构建的轻量级自动化BCS框架，它能够在复杂条件下以最少的计算资源实现一致、准确的评分。BCS-YOLO集成了Star-EMA模块和Star共享轻量级检测头（SSLDH），以提高检测精度并降低模型复杂性。Star-EMA模块采用多尺度注意力机制来平衡空间和语义特征，在杂乱的农场环境中优化奶牛后躯的特征表示。SSLDH进一步简化了检测头，使BCS-YOLO能够在资源有限的场景中部署。此外，基于通道的知识蒸馏生成聚焦于关键身体区域的软概率图，促进有效的知识转移并提高性能。在一个公共奶牛图像数据集上的结果表明，BCS-YOLO将模型大小减少了33%，并将平均精度均值（mAP）提高了9.4%。这些进展使BCS-YOLO成为大规模养殖中进行一致、准确的体况评分的强大非侵入性工具，支持可持续的畜牧管理，降低劳动力成本，提高动物福利，并提高生产力。
