Title: Direct Video-Based Spatiotemporal Deep Learning for Cattle Lameness Detection Show Chinese title

Title: 基于视频的时空深度学习用于牛跛行检测

Abstract: Cattle lameness is a prevalent health problem in livestock farming, often resulting from hoof injuries or infections, and severely impacts animal welfare and productivity. Early and accurate detection is critical for minimizing economic losses and ensuring proper treatment. This study proposes a spatiotemporal deep learning framework for automated cattle lameness detection using publicly available video data. We curate and publicly release a balanced set of 50 online video clips featuring 42 individual cattle, recorded from multiple viewpoints in both indoor and outdoor environments. The videos were categorized into lame and non-lame classes based on visual gait characteristics and metadata descriptions. After applying data augmentation techniques to enhance generalization, two deep learning architectures were trained and evaluated: 3D Convolutional Neural Networks (3D CNN) and Convolutional Long-Short-Term Memory (ConvLSTM2D). The 3D CNN achieved a video-level classification accuracy of 90%, with a precision, recall, and F1 score of 90.9% each, outperforming the ConvLSTM2D model, which achieved 85% accuracy. Unlike conventional approaches that rely on multistage pipelines involving object detection and pose estimation, this study demonstrates the effectiveness of a direct end-to-end video classification approach. Compared with the best end-to-end prior method (C3D-ConvLSTM, 90.3%), our model achieves comparable accuracy while eliminating pose estimation pre-processing.The results indicate that deep learning models can successfully extract and learn spatio-temporal features from various video sources, enabling scalable and efficient cattle lameness detection in real-world farm settings. Show Chinese abstract

Abstract: 牛跛行是畜牧业中一种普遍的健康问题，通常由蹄部损伤或感染引起，严重影晌动物福利和生产性能。早期和准确的检测对于减少经济损失和确保适当治疗至关重要。本研究提出了一种时空深度学习框架，利用公开的视频数据实现自动牛跛行检测。我们整理并公开发布了一组平衡的50个在线视频片段，涉及42头个体牛，从室内和室外多种视角录制。这些视频根据视觉步态特征和元数据描述分为跛行和非跛行类别。在应用数据增强技术以提高泛化能力后，训练和评估了两种深度学习架构：三维卷积神经网络（3D CNN）和卷积长短期记忆网络（ConvLSTM2D）。3D CNN在视频级别分类准确率达到90%，精确率、召回率和F1分数均为90.9%，优于ConvLSTM2D模型的85%准确率。与依赖多阶段流程（包括目标检测和姿态估计）的传统方法不同，本研究展示了直接端到端视频分类方法的有效性。与最佳端到端先前方法（C3D-ConvLSTM，90.3%）相比，我们的模型在消除姿态估计预处理的同时实现了相当的准确率。结果表明，深度学习模型可以成功地从各种视频源中提取和学习时空特征，从而在现实农场环境中实现可扩展且高效的牛跛行检测。