Semantic segmentation approach for tunnel roads’ analysis

Abstract

Image segmentation is the most important step for any visual scene understanding system. In this paper, we use a semantic approach where each pixel is labeled with a semantic object category. Location of objects inside a tunnel’s road is a crucial task for an automatic tunnel incident detection system. It needs in particular to accurately detect and localize different types of zones, such as road lane, emergency lane, and sidewalk. Unfortunately, the existing methods often fail in providing acceptable image regions due to dynamic environment conditions: change in the lighting conditions, shadow appearance, objects variability, etc. To overcome these difficulties, we proposed to use the semantic tunnel image segmentation approach and a Convolutional Neural Network (CNN) to solve this problem. To evaluate the performance of the proposed approach, we performed a comparison to the state of the art and recent methods on two different datasets collected from two tunnels in France, called the ”T1” and ”T2”. Our extensive study leads to the provide of the best tunnel scene segmentation approach. The proposed method has been deployed by VINCI Autoroutes company in a real-world environment for automatic incident detection system.