The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 599–603, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-599-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 599–603, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-599-2020

  21 Aug 2020

21 Aug 2020

AUTOMATIC TRAFFIC SIGN DETECTION AND RECOGNITION USING MOBILE LIDAR DATA WITH DIGITAL IMAGES

H. Guan1, Y. Yu2, D. Li3, and J. Li4 H. Guan et al.
  • 1School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China
  • 2College of Computer Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China
  • 3State Key laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
  • 4Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Keywords: mobile LiDAR, images, traffic sign, capsule convolutional networks, high-order capsule features

Abstract. This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest regions are first extracted from mobile LiDRA data. Next, traffic signs are identified from digital images simultaneously collected from the multi-sensor mobile LiDAR systems via a convolutional capsule network model. The experimental results demonstrate that the proposed method obtains a promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.