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| Multi-objective classification method of nursery scene based on 3D laser point cloud |
Hui LIU( ),Xiu-li WANG,Yue SHEN,Jie XU |
| School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China |
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Abstract To improve the accuracy of multi-target classification and recognition tasks on the nursery scene, a multi-objective classification method based on fusion convolutional block attention module and PointNet++ was proposed. The attention mechanism module was embedded in the feature extraction layer of the original PointNet++ network to enhance the extraction of key features and weaken the useless features, the parameter numbers were reduced while the network feature learning capability was improved. The LeakyReLu function was introduced as the activation function model, and good nonlinear transformation effects were obtained. To verify the classification performance of the proposed method on the nursery scene, a dataset was created using laser to acquire multiple objects (trees with different forms) and non-objects (pedestrians, signs, planting pots, etc.) from the nursery, and the classification experiments were conducted in the dataset. Experimental results showed that the proposed method achieved an overall classification accuracy of 96.38% and the classification speed reached 0.04 frame/s in the dataset, both of which were better than the original PointNet++ network’s.
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Received: 27 February 2023
Published: 27 December 2023
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| Fund: 国家自然科学基金资助项目(32171908)；江苏高校优势学科资助项目（PAPD） |
基于三维激光点云的苗圃场景多目标分类方法
为了提升苗圃场景中多目标分类和识别的准确率，提出基于融合卷积块注意力模块的PointNet++多目标分类方法. 通过在原PointNet++网络的特征提取层中嵌入注意力机制模块，增强对关键特征的提取，弱化无用特征，降低参数量的同时提高网络特征学习能力. 引入LeakyReLu函数作为激活函数模型，获得的非线性变换效果良好. 为了验证所提方法在苗圃中的分类性能，使用激光采集多种靶标(不同形态的树木)与非靶标(行人、指示牌、种植盆等)制作数据集，并在该数据集上进行分类实验. 实验结果表明，所提方法在苗圃数据集上的分类总体精度达到96.38%，分类速度达到0.04帧/s，均优于原PointNet++网络.
关键词：
农业机器人,
深度学习,
点云,
目标分类,
PointNet++,
注意力机制
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