A transfer learning-based network model integrating kernel convolution with graph attention mechanism for point cloud segmentation of livestock

•We propose a novel part segmentation network model.•We train the pre trained model using pig point cloud data.•We use pre trained model for point cloud segmentation of cattle.•Experiments show that our model exhibits better segmentation performance. Non-contact body size measurement has become a hot research topic in intelligent livestock farming. In regard to body size measurement of large livestock, such as cattle, collecting a substantial number of point clouds is frequently involved. The direct calculation of all point clouds for body size measurement can be impacted as point clouds of different body parts may interfere with each other, which poses huge challenges for the positioning of key points and induces inaccurate positioning, resulting in measurement errors. The accuracy of body size measurement can be improved by segmenting point clouds of different body parts from each other, key measurement points can be precisely located, thus enhancing the accuracy of body size measurement. In this paper, we propose a network model initially trained for pig point cloud segmentation based on the Kernel Convolution integrated with Graph Attention Mechanism (KCGATNet for short), which, through transfer learning techniques, can also be used to achieve successful segmentation of various cattle point clouds using only 7 training samples. The model utilizes two core modules, Kernel Convolution (KC) and Point-based Graph Attention Mechanism (P-GAT), to extract local neighborhood features of point clouds. When using pig body point clouds as training data, it achieved precise segmentation of the head, ears, limbs, torso, and tail of pigs through a downsampling-upsampling architecture. On the test set of pig point clouds, Overall Accuracy (OA) reached 98.1% and mean Intersection over Union (mIoU) was up to 90.5%. Furthermore, when this model served as a pre-trained model and underwent transfer learning using 7 sets of annotated data of Simmental cattle, it achieved a mIoU of 90.1% on a test set of 93 Simmental cattle, 89.6% on a test set of 439 dairy buffalo, 90.2% on a test set of 103 Hereford cattle, and 90.0% on a test set of 119 Black Angus cattle. The experimental outcomes fully demonstrate the robustness of the proposed livestock point cloud segmentation model, KCGATNet. With transfer learning of a small sample size, it can reliably perform point cloud segmentation on a wide range of different breeds of quadrupedal livestock, saving a significant amount of time