High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 16; p. 5275
• Main Authors: Omotara, Gbenga, Tousi, Seyed Mohamad Ali, Decker, Jared, Brake, Derek, DeSouza, G N
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14-08-2024; MDPI
• Subjects: 3D surface reconstruction; Accuracy; Animals; Automation; Calibration; Cameras; Cattle; cattle scanner; Deep Learning; Farming; Imaging, Three-Dimensional - methods; Livestock; R&D; Registration; Research & development; Scanners; segmentation; Sensors; Software; deep learning; segmentation; 3D surface reconstruction; cattle scanner
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24165275

We introduce a high-throughput 3D scanning system designed to accurately measure cattle phenotypes. This scanner employs an array of depth sensors, i.e., time-of-flight (ToF) sensors, each controlled by dedicated embedded devices. The sensors generate high-fidelity 3D point clouds, which are automatically
using a point could segmentation approach through deep learning. The deep learner combines raw RGB and depth data to identify correspondences between the multiple 3D point clouds, thus creating a single and accurate mesh that reconstructs the cattle geometry on the fly. In order to evaluate the performance of our system, we implemented a two-fold validation process. Initially, we quantitatively tested the scanner for its ability to determine accurate volume and surface area measurements in a controlled environment featuring known objects. Next, we explored the impact and need for multi-device synchronization when scanning moving targets (cattle). Finally, we performed qualitative and quantitative measurements on cattle. The experimental results demonstrate that the proposed system is capable of producing high-quality meshes of untamed cattle with accurate volume and surface area measurements for livestock studies.