Abstract
Abstract. High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.
Highlights
In recent years, an extensive study has been going on regarding the practicability of automated driving
We present a novel automatic road sign recognition technique utilizing both images and 3D point cloud acquired by MMS
The proposed approach consists of three stages: 1) detection of road signs from high resolution images based on their color and shape features using object based image analysis method, 2) filtering out of false positives with size and position information estimated from point cloud, regions of interest (ROI) of candidates and focal length of lens, and 3) road sign recognition using template matching method after shape normalization
Summary
An extensive study has been going on regarding the practicability of automated driving. Highdefinition and highly accurate road maps are necessary for the realization of automated driving. A technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of MMS, it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. Road sign recognition algorithms using images usually consist of two stages: road sign detection and road sign classification. Classification shall be conducted using traditional template matching (Siogkas and Dermatas, 2006) or via techniques from the field of machine learning, such as Support Vector Machines (Maldonado-Bascon et al, 2007; Adam and Ioannidis, 2014) and deep learning (Sermanet and LeCun, 2012; Ciresan et al, 2012). Ciresan et al, (2012) presented a state-of-the-art road sign classification approach using deep neural network, which won the German
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
