This paper presents a method for classification and localization of road signs in a 3D space, which is done with a help of neural network and point cloud obtained from a laser range finder (LIDAR). In addition, to accomplish this task and train the neural network (which is based on Faster-RCNN architecture) a dataset was collected. The trained convolutional network is used as a part of ROS node which fuses the obtained classification, data from the camera and lidar measurements. The output of the system is a set of images with bounding boxes and point clouds, corresponding to real signs on the road. The introduced method was tested and performed well on a dataset acquired from a self-driving car during different road conditions.

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104125 (15 March 2019); doi: 10.1117/12.2523155

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)