College of Information Engineering, Northwest A&F University, Yangling, Xianyang 712100, China.
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Xianyang 712100, China.
Sensors (Basel). 2018 Sep 9;18(9):3014. doi: 10.3390/s18093014.
The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of -means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.
大动物的体型测量在提高质量和遗传育种方面起着重要作用，而基于计算机视觉的远程非接触测量可以在危险的应激反应和耗时的手动测量方面取得重大进展。本文提出了一种新的基于三维数字建模的活体秦川牛体型测量方法。该方法基于激光雷达（LiDAR）传感器获取活体牛的原始点数据序列，融合条件、统计离群值和体素网格滤波方法来去除背景和离群值。通过均值聚类提取和随机抽样一致性（RANSAC）算法分割后，提出快速点特征直方图（FPFH）算法自动获取牛的数据。利用双向随机 K-D 树和贪心投影三角剖分（GPT）重建方法，快速迭代最近点（ICP）匹配重建牛的表面，得到 3D 牛模型，从而可以点击和计算牛轮廓的特征点。最后，在现场测量了 5 个体型参数（鬐甲高、胸深、背高、体斜长和腰高），并验证了其精度在 2mm 以内，误差接近 2%。实验结果表明，该方法可以作为一种新的可行的非接触式大型牲畜体型测量方法。
