COMPARATIVE ANALYSIS OF MORPHOLOGICAL (MCSS) AND LEARNING-BASED (SPG) STRATEGIES FOR DETECTING SIGNAGE OCCLUSIONS ALONG TRANSPORTATION CORRIDORS

Abstract

Signage visibility along transportation corridors is critical for drivers in terms of road safety, traffic flow, and enforcement. Traffic signs that are easy to recognize by drivers and autonomous vehicles can help in avoiding accidents and improve safety. Nowadays, Mobile Mapping Systems (MMS) equipped with LiDAR units can scan road network components and its surrounding environment at a normal driving speed while collecting accurate geospatial data. Most traffic signs have well-defined geometric characteristics (e.g., linear or planar features) which can be identified in the 3D LiDAR data acquired by MMS. Therefore, MMS LiDAR data are an ideal source to recognize traffic signs. In addition to traffic sign detection, MMS can also identify vegetation along the right-of-way and evaluate signage visibility. Thus, this paper presents a framework for using MMS LiDAR data for traffic sign and vegetation detection which is a prerequisite for signage visibility analysis. For signage and vegetation detection, two alternative strategies are adopted: 1) a morphological approach and 2) a learning-based approach. For the geometric/morphological approach, Multi-Class Simultaneous Segmentation (MCSS) is utilized in this study. As for the learning-based strategy, semantic segmentation of LiDAR data are performed using Super Point Graph (SPG). Lastly, signage visibility analysis is conducted based on the occlusion rate assessed from different driver’s viewpoints.