Title: Assessing the Feasibility of Internet-Sourced Video for Automatic Cattle Lameness Detection Show Chinese title

Title: 评估互联网来源视频在自动牛跛行检测中的可行性

Abstract: Cattle lameness is often caused by hoof injuries or interdigital dermatitis, leads to pain and significantly impacts essential physiological activities such as walking, feeding, and drinking. This study presents a deep learning-based model for detecting cattle lameness, sickness, or gait abnormalities using publicly available video data. The dataset consists of 50 unique videos from 40 individual cattle, recorded from various angles in both indoor and outdoor environments. Half of the dataset represents naturally walking (normal/non-lame) cattle, while the other half consists of cattle exhibiting gait abnormalities (lame). To enhance model robustness and generalizability, data augmentation was applied to the training data. The pre-processed videos were then classified using two deep learning models: ConvLSTM2D and 3D CNN. A comparative analysis of the results demonstrates strong classification performance. Specifically, the 3D CNN model achieved a video-level classification accuracy of 90%, with precision, recall, and f1-score of 90.9%, 90.9%, and 90.91% respectively. The ConvLSTM2D model exhibited a slightly lower accuracy of 85%. This study highlights the effectiveness of directly applying classification models to learn spatiotemporal features from video data, offering an alternative to traditional multi-stage approaches that typically involve object detection, pose estimation, and feature extraction. Besides, the findings demonstrate that the proposed deep learning models, particularly the 3D CNN, effectively classify and detect lameness in cattle while simplifying the processing pipeline. Show Chinese abstract

Abstract: 牛的跛行通常由蹄部损伤或趾间皮炎引起，导致疼痛并对行走、进食和饮水等基本生理活动产生显著影响。 本研究提出了一种基于深度学习的模型，用于使用公开可用的视频数据检测牛的跛行、疾病或步态异常。 该数据集包括来自40头不同个体牛的50个独特视频，这些视频从室内外的不同角度录制。 数据集中一半代表正常行走（非跛行）的牛，另一半则包括表现出步态异常（跛行）的牛。 为了增强模型的鲁棒性和泛化能力，在训练数据上应用了数据增强技术。 然后使用两个深度学习模型——ConvLSTM2D和3D CNN——对预处理后的视频进行分类。 结果的比较分析显示出了强大的分类性能。 具体来说，3D CNN模型达到了90.9\%的视频级分类准确率，而ConvLSTM2D模型的准确率为85\%。 这些分类模型能够从视频数据中学习时空特征，为传统的多阶段方法提供了替代方案，通常这些方法包括对象检测、姿态估计和特征提取。 此外，研究结果表明，所提出的深度学习模型，特别是3D CNN，在简化处理流程的同时，能有效分类和检测牛的跛行。