用神经网络从奶牛乳头视频中找关键帧，提升乳腺炎风险评估效率。
Supervised Learning Model for Key Frame Identification from Cow Teat Videos
- 融合距离与集成模型，精准识别乳头完整的关键帧。
- 相比单一方法，F-score 明显提升，增强识别稳定性。
- 适合畜牧健康监测系统开发者及动物医学研究者使用。
本文提出一种基于神经网络和视频分析的方法，以提高通过视频判断奶牛乳腺炎风险的准确性。乳腺炎是奶牛乳腺组织的感染，可通过检查乳头发现。传统上，兽医在挤奶过程中评估乳头健康，但时间有限，影响判断精度。在商业化牧场中，奶牛在挤奶厅被摄像头记录，本文利用神经网络识别视频中奶牛乳房完整的关 键帧，使兽医能更灵活地进行健康评估，提升效率与准确性。然而，使用奶牛乳头视频进行评估面临诸多挑战：复杂环境、奶牛姿势变化大、难以从视频中准确识别乳房。为解决这些问题，本文提出融合距离与集成模型，以提升关键帧识别的性能（F-score）。实验结果表明，这两种方法相较于单一距离度量或模型，显著提升了识别效果。
原文摘要 · Abstract (English)
This paper proposes a method for improving the accuracy of mastitis risk assessment in cows using neural networks and video analysis. Mastitis, an infection of the udder tissue, is a critical health problem for cows and can be detected by examining the cow's teat. Traditionally, veterinarians assess the health of a cow's teat during the milking process, but this process is limited in time and can weaken the accuracy of the assessment. In commercial farms, cows are recorded by cameras when they are milked in the milking parlor. This paper uses a neural network to identify key frames in the recorded video where the cow's udder appears intact. These key frames allow veterinarians to have more flexible time to perform health assessments on the teat, increasing their efficiency and accuracy. However, there are challenges in using cow teat video for mastitis risk assessment, such as complex environments, changing cow positions and postures, and difficulty in identifying the udder from the video. To address these challenges, a fusion distance and an ensemble model are proposed to improve the performance (F-score) of identifying key frames from cow teat videos. The results show that these two approaches improve performance compared to using a single distance measure or model.
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