基于RGB和深度视频的分割增强型奶牛跛行检测
Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video
摘要：
奶牛跛行是影响奶牛生存周期和生活质量的一种严重疾病，给奶牛生产造成巨大的经济损失。早期跛行检测有助于农民及早解决疾病，避免奶牛状况恶化造成的负面影响。我们收集了一个奶牛通过走廊离开挤奶站的短片段的数据集，并注释了奶牛的跛行程度。本文探讨了由此产生的数据集，并提供了一个详细的描述数据汇集过程。此外，我们提出了一种跛行检测方法，该方法利用预先训练的神经网络从视频中提取区分特征，并为每头奶牛分配一个二进制分数，指示其状况：“健康”或“跛行”。“我们通过强制模型专注于牛的结构来改进这种方法，我们通过用经过训练的分割模型预测的二进制分割掩码替换RGB视频来实现。这项工作的目的是鼓励研究和提供见解的适用性计算机视觉模型的奶牛跛行检测在农场。
英文摘要：
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.
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