Road sign detection and localization based on camera and lidar data

Alexander Buyval; Aidar Gabdullin; Maxim Lyubimov

doi:10.1117/12.2523155

15 March 2019 Road sign detection and localization based on camera and lidar data

Alexander Buyval, Aidar Gabdullin, Maxim Lyubimov

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104125 (2019) https://doi.org/10.1117/12.2523155
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

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.

Citation Download Citation

Alexander Buyval, Aidar Gabdullin, and Maxim Lyubimov "Road sign detection and localization based on camera and lidar data", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104125 (15 March 2019); https://doi.org/10.1117/12.2523155

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