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