Automatic livestock dimension measurement driven by omnidirectional scanning selective state-space segmentation
Keywords:
Point cloud segmentation, Deep learning, Selective state space, Livestock body measurementAbstract
Existing methods often struggle to accurately and automatically separate livestock from complex background environments and are further hindered by severe noise interference in point cloud data, which leads to insufficient segmentation accuracy and ultimately affects the precision of livestock body measurements. To address these challenges, this study proposes a livestock body measurement method based on omnidirectional spatial localization segmentation. First, an omnidirectional point cloud segmentation model (Omni-PointMamba) was designed, which adopts an eight-directional scanning strategy in 3D space and an alternating dual-module architecture that integrates Point Mamba Blocks with convolutional modules. By enhancing spatial neighborhood modeling capabilities, the model efficiently fuses region-specific geometric features with comprehensive structural context, thereby achieving precise separation of livestock from background interference. Second, a spatial localization-based measurement method was developed. This method constructs a spatial coordinate system using ground normal vectors to achieve automatic rotation and alignment of the point cloud. It employs a two-step clustering strategy to identify key body parts, including the head and tail, and determines measurement landmarks through geometric point distribution analysis, enabling accurate, non-contact estimation of body size parameters. Experiments conducted on 20 pigs and 103 cattle demonstrate that the proposed model achieves outstanding segmentation performance, with mean Intersection over Union (mIoU) values of 0.9930 for pigs and 0.9685 for cattle. Furthermore, the model yields low mean absolute percentage errors (MAPE) in morphological measurements. For pigs, the MAPE for body width, hip width, and chest girth are 1.34%, 1.99%, and 1.37%, respectively. For cattle, the MAPE for body slant length, chest width, hip height, and heart girth are 2.24%, 3.28%, 2.18%, and 4.22%, respectively. These results indicate that the proposed method provides highly accurate segmentation and morphological measurement capabilities for both pigs and cattle.
Keywords: point cloud segmentation; deep learning; selective state space; livestock body measurement
DOI: 10.25165/j.ijabe.20261903.9846
Citation: Zhang K, Ma Q, Shi X C. Automatic livestock dimension measurement driven by omnidirectional scanning selective state-space segmentation. Int J Agric & Biol Eng, 2026; 19(3): 99–109.
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