期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 11, 期 5, 页码 1715-1724出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2018.2810143
关键词
Digital images; deep learning; geometrical features; intensity; mobile LiDAR point clouds; traffic signs
类别
资金
- Natural Science Foundation of Jiangsu Province [BK20151524, BK20160427]
- National Natural Science Foundation of China [41501501, 61603146, 41671454]
- Natural Science Research in Colleges and Universities of Jiangsu Province [16KJB520006]
- Science and Technology Project of Huaian City [HAG201602]
- Jiangsu Shuangchuang project
This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected traffic-sign points onto the digital images. Afterward, the segmented traffic-sign images are normalized for automatic classification with a given image size. Finally, a traffic-sign classifier is proposed based on a supervised Gaussian-Bernoulli deep Boltzmann machine model. We evaluated the proposed method using datasets acquired by a RIEGL VMX-450 system. The traffic-sign detection accuracy of 86.8% was achieved; through parameter sensitivity analysis, the overall performance of traffic-sign classification achieved a recognition rate of 93.3%. The computational performance showed that our method provides a promising solution to traffic-sign detection and classification using mobile LiDAR point clouds and digital images.
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