Tun San Chain, Onizuka Tsubasa, Tin Pyke, Aikawa Masaru, Kobayashi Ikuo, Zin Thi Thi
Graduate School of Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.
Organization for Learning and Student Development, University of Miyazaki, Miyazaki 889-2192, Japan.
J Imaging. 2024 Mar 8;10(3):67. doi: 10.3390/jimaging10030067.
This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow's body, with a specific emphasis on the back region's maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow's depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow's backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management.
本研究创新了家畜健康管理方式，通过将顶视深度相机与3D深度相机及深度学习相结合，实现了对奶牛跛足的准确检测、分类和精确分割，这使其有别于二维系统。该研究强调了早期检测奶牛跛足的重要性，并着重从奶牛身体提取深度数据，特别关注背部区域的最大值。通过Detectron2框架和交并比（IOU）技术实现了对奶牛的精确检测和跟踪。在为期三天的测试期内，每天对不同数量的奶牛（每天56至64头）进行两次观察，该研究始终取得了令人印象深刻的平均检测准确率99.94%。在相同观察期内，跟踪准确率保持在99.92%。随后，该研究利用从检测结果和原始深度图像中得到的二值掩码图像提取奶牛的深度区域。特征提取基于奶牛脊柱区域的最大高度测量生成一个特征向量。这个特征向量被用于分类，评估了三种分类器：随机森林（RF）、K近邻（KNN）和决策树（DT）。该研究突出了顶视深度摄像机在准确检测和分类奶牛跛足方面的潜力，对家畜健康管理具有重要意义。
