Vision-based mobile analysis of roadside guardrail structures

Abstract

Vision-based analysis of the roadside infrastructure is a research field of growing relevance, since autonomous driving, roadside asset digitization and mapping are key emerging applications. The advancement of Deep Learning for vision-based environment perception represents a core enabling technology to interpret scenes in terms of its objects and their spatial relations. In this paper we present a multi-sensory mobile analysis systemic concept, which targets the structural classification of roadside guardrail structures, and allows for digital measurements within the scene surrounding the guardrail objects. We propose an RGB-D vision-based analysis pipeline to perform semantic segmentation and metric dimension estimation of key structural elements of a given guardrail segment. We demonstrate that the semantic segmentation task can be fully learned in the synthetic domain and deployed with a high accuracy in the real domain. Based on guardrail structural measurements aggregated and tracked over time, our pipeline estimates one or several type-labels for the observed guardrail structure, based on a prior catalog of all possible types. The paper presents qualitative and quantitative results from experiments using our measurement vehicle and covering 100km in total. Obtained results demonstrate that the presented mobile analysis framework can well delineate roadside guardrail structures spatially, and able to propose a limited set of type-candidates. The paper also discusses failure modes and possible future improvements towards accomplishing digital mapping and recognition of safety-critical roadside assets.