摘要
[目的/意义]为了提高体尺关键点定位准确率,猪三维点云体尺自动测量方法会采用点云分割,在各个分割后局部点云定位测量关键点,以减少点云之间相互干扰。然而点云分割网络通常需要消耗较大计算资源,且现有测量点定位效果仍有待提升空间。本研究旨在通过设计关键点生成网络从猪体点云中提取出各体尺测量所需关键点。在降低显存资源需求的同时提高测量关键点定位效果,提高体尺测量的效率和精度。[方法]针对猪三维表面点云进行体尺测量,提出了一种定位猪体尺关键点的模型Pig Back Transformer。模型分为两个模块,分别设计了两种改进的Transformer自注意力编码器,第一模块为全局关键点模块,首先设计了一种猪背部边缘点提取算法用于获取边缘点,再使用edge encoder编码器以边缘点集合作为输入,edge encoder的edge attention中加入了边缘点和质点的偏移距离信息;第二模块为关键点生成模块,使用了back attention机制的back encoder,其中加入了与质心和第一模块生成的全局关键点的偏移量,并将偏移量与点云注意力通过按位max pooling操作结合,最后通过生成猪的体尺测量关键点和背脊走向点。最后设计了使用关键点和背脊走向点作为输入的体尺算法。[结果和讨论]对比关键点和背脊走向点生成任务上Pig Back Transformer表现最佳,并对比体尺计算结果与人工测量结果,体长相对误差为0.63%,相对PointNet++、Point Transformer V2、Point Cloud Transforme、OctFormer PointTr等模型有较大提升。[结论] Pig Back Transformer能相对准确地生成猪体尺关键点,提高体尺测量数据准确度,并且通过点云特征定位体尺关键点节省了计算资源,为无接触牲畜体尺测量提供了新思路。
[Objective]Nowadays most no contact body size measurement studies are based on point cloud segmentation method,they use a trained point cloud segmentation neural network to segment point cloud of pigs,then locate measurement points based on them.But point cloud segmentation neural network always need a larger graphics processing unit(GPU)memory,moreover,the result of the measurement key point still has room of improvement.This study aims to design a key point generating neural network to extract measurement key points from pig's point cloud.Reducing the GPU memory usage and improve the result of measurement points at the same time,improve both the efficiency and accuracy of the body size measurement.[Methods]A neural network model was proposed using improved Transformer attention mechanic called Pig Back Transformer for generating key points and back orientation points which were related to pig body dimensions.In the first part of the network,it was introduced an embedding structure for initial feature extraction and a Transformer encoder structure with edge attention which was a self-attention mechanic improved from Transformer's encoder.The embedding structure using two shared multilayer perceptron(MLP)and a distance embedding algorithm,it takes a set of points from the edge of pig back's point cloud as input and then extract information from the edge points set.In the encoder part,information about the offset distances between edge points and mass point which were their feature that extracted by the embedding structure mentioned before incorporated.Additionally,an extraction algorithm for back edge point was designed for extracting edge points to generate the input of the neural network model.In the second part of the network,it was proposed a Transformer encoder with improved self-attention called back attention.In the design of back attention,it also had an embedding structure before the encoder structure,this embedding structure extracted features from offset values,these offset values were calculated by th
作者
王宇啸
石源源
陈招达
吴珍芳
蔡更元
张素敏
尹令
WANG Yuxiao;SHI Yuanyuan;CHEN Zhaoda;WU Zhenfang;CAI Gengyuan;ZHANG Sumin;YIN Ling(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China;National Engineering Research Center For Breeding Swine Industry,Guangzhou 510642,China;College of Animal Science,South China Agricultural University,Guangzhou 510642,China;National Key Laboratory of Pig and Poultry Breeding Industry,Guangzhou 510640,China)
出处
《智慧农业（中英文）》
CSCD
2024年第4期76-90,共15页
Smart Agriculture
基金
国家自然科学基金面上基金(32172780)
国家重点研发计划子课题(2023YFD1300202)。
