基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法  

Real-time recognizing spray target in forest and fruit orchard using lightweight PointNet

作　　者：刘慧[1] 杜志鹏 杨锋 张钰[1] 沈跃[1] LIU Hui;DU Zhipeng;YANG Feng;ZHANG Yu;SHEN Yue(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,镇江212013

出　　处：《农业工程学报》2024年第8期144-151,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(32171908)。

摘　　要：为了进一步提高喷雾机器人靶标检测的精准性、实时性和应用部署的实用性,该研究提出一种基于轻量化PointNet网络的林果园喷雾作业靶标实时识别方法。首先通过区域提取降采样、地面分割和改进DBSCAN聚类等点云预处理方法提取原始点云中的靶标;然后通过移动最小二乘上采样将靶标点云转化为满足点云识别网络输入要求的点云数据;最终通过在PointNet网络中引入残差模块和改进循环剪枝算法轻量化PointNet网络,完成林果树靶标的实时识别。试验结果表明,在ModelNet40数据集上,轻量化PointNet网络可达89.7%的准确率;在实际苗圃环境的试验中,该研究方法对靶标的识别准确率可达92.49%,同时误识率与拒识率分别为13.4%和6.47%,相较PointNet网络识别准确率提升了4.38个百分点,误识率和拒识率分别降低了7.2和4.07个百分点;轻量化PointNet网络识别准确率仅比PointNet++网络低1.14个百分点,误识率和拒识率分别高了0.9和1.12个百分点。但是轻量化PointNet网络的模型参数量较PointNet网络和PointNet++网络的模型参数量显著减少,仅为PointNet网络的11.5%,PointNet++网络的27.02%;运算量相较PointNet网络、PointNet++网络分别减少13.3和76.79个百分点。该研究提出的轻量化PointNet网络具有较高的实时性、精确性和鲁棒性,能够满足林果园喷雾作业的靶标识别需求,可为林果园喷雾作业靶标实时识别提供参考。This study aims to enhance the accuracy,real-time performance,and practicality of real-time target detection in spray robots in forest and fruit orchards.A novel approach was introduced using a lightweight PointNet deep learning network.Efficient detection of target was realized during spray under complex natural environments.Several key steps were consisted as follows.Firstly,the target was extracted from the original point cloud.Preprocessing techniques included the region extraction downsampling,ground segmentation,and an improved density-based spatial clustering of application with noise.The target points were isolated to facilitate the subsequent recognition.After extraction,the target point cloud was transformed into the format suitable for the input into the point cloud recognition network.The moving least squares upsampling was carried out to collect the appropriately prepared data.The recognition network of point cloud was constructed to specifically designed for the forest and fruit tree environments.The residual module was introduced to enhance the cyclic pruning algorithm into the PointNet network.The representation power of the network was improved to capture more complex patterns and features of target objects.The cyclic pruning algorithm was used to refine the network architecture,and then remove unnecessary connections,in order to reduce the computational overhead with high performance.The experimental results demonstrate that an impressive accuracy of 89.7%was achieved in the widely-used ModelNet40 dataset.The accuracy of target recognition reached 92.49%in actual nursery environments.Moreover,the misidentification and rejection rates were reduced to 13.4%and 6.47%,respectively.The accuracy of the improved PointNet was outperformed by 4.38 percentage points,compared with the original.While the misidentification and rejection rates were reduced significantly.Comparative analysis revealed that the recognition accuracy of the improved model was only 1.14 percentage points lower than that of PointNet

关 键 词：喷雾 机器人 林果园 点云预处理 轻量化PointNet网络 循环剪枝 

分 类 号：S491[农业科学—植物保护]