Overview of LiDAR point cloud target detection methods based on deep learning
ISSN: 0260-2288
Article publication date: 23 August 2022
Issue publication date: 30 August 2022
Abstract
Purpose
The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.
Design/methodology/approach
Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.
Findings
Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.
Originality/value
This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China (62171467).
Citation
Huang, S., Liu, L., Fu, X., Dong, J., Huang, F. and Lang, P. (2022), "Overview of LiDAR point cloud target detection methods based on deep learning", Sensor Review, Vol. 42 No. 5, pp. 485-502. https://doi.org/10.1108/SR-01-2022-0022
Publisher
:Emerald Publishing Limited
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