| Abstract: |
Simple Summary: Cattle farming occupies an important position in the global livestock industry. The body size of cattle is a major indicator of their growth. The traditional manual measurement of cattle body sizes is both costly and time-consuming, and current automated measurement methods are susceptible to environmental factors. In order to solve these problems, this study introduces a multi-view fusion-driven automated measurement system. In this study, non-contact cattle body size measurement was achieved using multi-view RGB images, depth images, and point clouds. Environmental interference was excluded as much as possible during the process. By using multi-view Zed2 binocular cameras and advanced algorithms, accurate body size measurements were achieved, with small errors compared to manual methods. This demonstrates the potential of the model to improve efficiency and animal welfare in the cattle industry. 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. [ABSTRACT FROM AUTHOR] |
