论文标题
RGB和深度视频中的奶牛中的分割增强了la脚检测
Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video
论文作者
论文摘要
牛la脚是一种严重的疾病，会影响奶牛的生命周期和生活质量，并导致巨大的经济损失。早期的la悔检测有助于农民尽早解决疾病，并避免由牛的变性引起的负面影响。我们收集了一个简短的奶牛的数据集，穿过走廊，从走廊出发，并注释了母牛的la行程。本文探讨了结果数据集，并提供了数据收集过程的详细说明。此外，我们提出了一种la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歧视性特征，并将二进制分数分配给每个牛的二进制分数：“健康”或“ la脚”。我们通过强迫模型专注于牛的结构来改善这种方法，我们通过用训练有素的分割模型预测的二元分割掩码来代替RGB视频来实现。这项工作旨在鼓励研究并提供有关计算机视觉模型在农场上检测的适用性的见解。
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.
