Extraction of key regions of beef cattle based on bidirectional tomographic slice features from point cloud data

文献类型： 外文期刊

第一作者： Li, Jiawei

作者： Li, Jiawei;Li, Jiawei;Ma, Weihong;Zhao, Chunjiang;Li, Qifeng;Ding, Luyu;Gao, Ronghua;Yu, Ligen;Tulpan, Dan;Yang, Simon X.;Wang, Zhiquan

作者机构：

关键词： Beef cattle point cloud; Principal direction calibration; Region Segmentation; Tomographic slice

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 199 卷

页码：

收录情况： SCI

摘要： As the world population grows and the increase in food consumption creates unprecedented pressure on beef producers, innovative technologies for sustainable and efficient beef production is quintessential. Therefore, a significant effort is ongoing on developing digital precision technologies for continuous beef cattle monitoring and accurate cattle growth estimation. One such technology relies on 3D visual sensor technologies. 3D point cloud data acquired with 3D cameras can be processed to evaluate the livestock's growth condition with minimal human intervention. Essentially, the automatic measurement of animal body dimensions highly depends on the location accuracy of the key points or regions in point clouds. However, the location of specific points and regions requires extra assistance and usually leads to semi-automatic and low-accuracy solutions at best. To address these issues, we demonstrated a novel strategy for partial segmentation of beef cattle point clouds, based on the Bidirectional Tomographic Slice Segmentation (BTSS) algorithm. The cattle point clouds can be successfully segmented into the head and neck, front trunk, middle trunk, back trunk, lower leg, and hip and tail regions with high accuracies of 89%, 91%, 94%, 95%, 92%, and 95%, respectively. Consequently, the segmentation completion rate is 96%, and the average segmentation accuracy reaches 92.8%. When compared to traditional approaches, our method efficiently extracted abundant body characteristics from the beef cattle point cloud. Moreover, the proposed algorithm exhibited generalization capabilities to segment the point clouds of other cloven-hoofed livestock species. The accurate localization of key regions enabled body dimensions measurements and non-contact weighing, which can provide a solid support for breeding needs such as health evaluation, production performance measurement, and genetic breeding evaluation.

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