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

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
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Abstract

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 spent on manual annotation and improving the efficiency of livestock point cloud segmentation models.

基于迁移学习的网络模型,将核卷积与图注意机制相结合,用于牲畜点云分割
非接触式体型测量已成为智能畜牧业的热门研究课题。在对牛等大型牲畜进行体型测量时,经常需要采集大量的点云。直接计算所有点云进行体型测量会受到影响,因为不同身体部位的点云可能会相互干扰,这给关键点的定位带来了巨大挑战,导致定位不准确,从而造成测量误差。通过将不同身体部位的点云相互分割,可以精确定位关键测量点,从而提高体型测量的准确性。本文提出了一种基于内核卷积与图注意机制(Kernel Convolution integrated with Graph Attention Mechanism,简称 KCGATNet)的网络模型,该模型最初是为猪的点云分割而训练的,通过迁移学习技术,该模型也可用于仅使用 7 个训练样本就能成功分割各种牛的点云。该模型利用核卷积(KC)和基于点的图注意机制(P-GAT)两个核心模块提取点云的局部邻域特征。在使用猪体点云作为训练数据时,它通过下采样-上采样架构实现了对猪头、耳朵、四肢、躯干和尾巴的精确分割。在猪体点云测试集上,总体准确率(OA)达到了 98.1%,平均联合交叉率(mIoU)高达 90.5%。此外,当该模型作为预训练模型并使用 7 组西门塔尔牛注释数据进行迁移学习时,它在 93 头西门塔尔牛测试集中的 mIoU 达到了 90.1%,在 439 头奶水牛测试集中的 mIoU 达到了 89.6%,在 103 头赫里福德牛测试集中的 mIoU 达到了 90.2%,在 119 头黑安格斯牛测试集中的 mIoU 达到了 90.0%。实验结果充分证明了所提出的牲畜点云分割模型 KCGATNet 的鲁棒性。通过对小样本量的迁移学习,它可以可靠地对各种不同品种的四足牲畜进行点云分割,节省了大量的人工标注时间,提高了牲畜点云分割模型的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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