Distributed sensing and assessment of visual surface conditions for regional road networks
The objective of this research is to develop and verify a concept for distributed sensing and assessment of visual conditions of regional asphalt road networks. A mobile network of common passenger vehicles equipped with a backup camera, GPS and Internet access is used to capture area-wide geo-referenced pavement videos, which are processed in two stages. First, potential distresses are identified and located within a real-time rough detection procedure. Based on that, a distributed fine analysis procedure is used to classify and assess specific visual distress patterns. Within the proposed framework, novel methods are created to merge results of both stages in a statistically verified manner to draw conclusions on the classification reliability. In parallel, new concepts for the online learning of distress classifiers are developed to determine dynamically adoptable thresholds that are incrementally updated within the progressing distress assessment procedure.