计算机科学
点云
人工智能
激光雷达
点(几何)
深度学习
机器学习
数据挖掘
遥感
地理
数学
几何学
作者
Pedro Henrique Feijó de Sousa,Jefferson S. Almeida,Elene Firmeza Ohata,Fabrício G. Nogueira,Bismark C. Torrico,Victor Hugo C. de Albuquerque,Mohammad Mehedi Hassan,Neeraj Kumar,Md. Rafiul Hassan,Deepak Gupta
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-10-01
卷期号:23 (10): 19807-19816
被引量:2
标识
DOI:10.1109/tits.2021.3119132
摘要
Works that use point cloud avoid wasting time and cost of collection, using simulators and datasets available in the literature. In this way, there is access to an unlimited and organized amount of point clouds, an ideal setting for deep learning networks and Vehicular ad hoc networks (VANETs). However, models trained with synthetic data present problems when applied to real-world data.This work proposes the use of deep learning in the recognition of 3D objects captured with a Light Detection and Ranging (LIDAR), including a pre-processing stage. In addition, it is proposed two datasets, a real-world and a syntetic; each dataset includes three classes. A method of pre-processing is proposed to circumvent the distribution discrepancies of the proposed datasets and the existing datasets from literature, such as ModelNet. We use deep learning with the PointNet method, as it supports raw data from point clouds as input to the network. We performed three evaluation approaches: training and testing steps with the proposed datasets using (1) Lidar3DNetV1, which is a proposed network in this paper, (2) PointNet, and (3) classification of ModelNet datasets using Lidar3DNetV1. The proposed network achieved 98.33% of accuracy and a testing time of $88~\mu \text{s}$ in the synthetic dataset, while in the real-world dataset, the network reached 98.48% and $145~\mu \text{s}$ in accuracy and testing time, respectively.
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