Lameness is one of the major health problems on dairy farms, which seriously affects dairy cow welfare and increases the risk of premature culling. Accuracy lameness detection ensures timely treatment of hooves and improves the level of health management on dairy farms. Most of the existing lameness detection methods detect one dairy cow, and it is difficult to detect lameness in multiple dairy cows at the same time. In this paper, a lameness detection system based on instance segmentation is constructed to extract the lameness features of multiple dairy cows and automatically detect lameness. First, an improved SOLOv2 network is designed to reduce the semantic gap between low-level and high-level features and improve the precision of dairy cow segmentation. Second, individual matching of dairy cows in different video frames is performed using the Hungarian algorithm. Third, the Canny algorithm is used to extract the neck and back contour features and key gait features of dairy cows. Finally, a deep learning model is constructed, and multiple lameness features are fused to detect the lameness of dairy cows. To evaluate the performance of the constructed system, 172 videos were randomly selected from 246 videos as training videos, and the remaining 74 videos were selected as the test videos. The lameness detection accuracy of the constructed system was 98.65%. The experimental results showed that the constructed system can extract the lameness features of multiple dairy cows at the same time and accurately detect the lameness of dairy cows.

中文翻译：

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基于实例分割的奶牛跛行检测系统

跛行是奶牛场的主要健康问题之一，严重影响奶牛福利并增加过早扑杀的风险。准确的跛行检测保证了蹄子的及时处理，提高了奶牛场的健康管理水平。现有的跛行检测方法大多检测一头奶牛，很难同时检测多头奶牛的跛行。本文构建了一种基于实例分割的跛行检测系统，提取多头奶牛的跛行特征并自动检测跛行。首先，设计了改进的SOLOv2网络，以减少低级和高级特征之间的语义差距，提高奶牛分割的精度。其次，使用匈牙利算法对不同视频帧中的奶牛进行个体匹配。第三，利用Canny算法提取奶牛颈背部轮廓特征和关键步态特征。最后构建深度学习模型，融合多种跛行特征来检测奶牛的跛行情况。为了评估所构建系统的性能，从 246 个视频中随机选择 172 个视频作为训练视频，其余 74 个视频作为测试视频。构建的系统跛行检测准确率为98.65%。实验结果表明，构建的系统能够同时提取多头奶牛的跛行特征，准确检测奶牛的跛行情况。