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基于多视图融合的牛全姿态体型自动测量

Multi-View Fusion-Based Automated Full-Posture Cattle Body Size Measurement.

作者信息

Wu Zhihua, Zhang Jikai, Li Jie, Zhao Wentao

机构信息

School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014017, China.

出版信息

Animals (Basel). 2024 Nov 7;14(22):3190. doi: 10.3390/ani14223190.

Abstract

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

摘要

养牛业是全球畜牧业的重要组成部分,牛的体型是家畜生长的关键指标。然而,传统的手动测量体型的方法不仅耗时费力,而且成本高昂。同时,自动测量技术容易受到环境条件和家畜站立姿势的影响。为了克服这些挑战,本研究提出了一种多视图融合驱动的全姿态牛体自动测量系统。在户外自然光下,安装了三台Zed2相机,覆盖通道的不同视图。使用YOLOv8n算法从多个视图自动获取包括RGB图像、深度图像和点云在内的多幅图像。来自不同视图的点云经过多次去噪,成为牛体的局部点云。局部点云经过粗对齐和精对齐,成为牛体的完整点云。在通过YOLOv8x-pose算法检测到RGB图像上的二维关键点后,结合相机的内部参数和深度图中对应像素的深度值,将二维关键点映射到三维牛体上。基于映射后的三维关键点,自动测量不同姿势下奶牛的体型,包括身高、体长、腹围和胸围。此外,采用支持向量机和贝塞尔曲线对环境影响导致的周长体型缺失和变形进行校正。该自动体型测量系统对47头原产于中国的华西肉牛的身高、体长、腹围和胸围进行了测量,并将结果与手动测量结果进行了比较。与手动测量相比,平均相对误差分别为2.32%、2.27%、3.67%和5.22%,证明了该系统的可行性和准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e06f/11591069/87fa925e039f/animals-14-03190-g001.jpg

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