Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses. Early lameness detection helps farmers address illnesses early and avoid negative effects caused by the degeneration of cows' condition. We collected a dataset of short clips of cows passing through a hallway exiting a milking station and annotated the degree of lameness of the cows. This paper explores the resulting dataset and provides a detailed description of the data collection process. Additionally, we proposed a lameness detection method that leverages pre-trained neural networks to extract discriminative features from videos and assign a binary score to each cow indicating its condition: "healthy" or "lame." We improve this approach by forcing the model to focus on the structure of the cow, which we achieve by substituting the RGB videos with binary segmentation masks predicted with a trained segmentation model. This work aims to encourage research and provide insights into the applicability of computer vision models for cow lameness detection on farms.
翻译:早期的瘸子检测有助于农民及早应对疾病,避免奶牛病变造成的负面影响。我们收集了经过一个走廊的奶牛短片的数据集,并附加了牛的瘸子程度说明。本文探讨了由此形成的数据集,并详细描述了数据收集过程。此外,我们提议了一种瘸子检测方法,利用预先训练的神经网络从视频中提取歧视特征,并对每头奶牛进行二进制评分,说明其状况:“健康”或“健康”。我们改进了这一方法,将模型的重点放在牛的结构上,我们通过用经过训练的分解模型预测的双进制分解面罩取代RGB视频。这项工作旨在鼓励研究,并深入了解计算机视觉模型在农场检测牛瘸子时的适用性。