Abstract
Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.
Highlights
IntroductionRoad authorities worldwide are facing increasingly daunting challenges with regards to Pavement Maintenance and rehabilitation (M&R) programs
This paper aims at utilizing deep learning techniques namely the use of object detection within imagery collected, using low-cost smartphones, to gain a rapid assessment of the condition of a road network based on standardized distress techniques and severity determinations and to demonstrate the ability to use this process to continuously monitor the condition of the pavement structure
This paper was aimed at presenting a deep learning pipeline and framework for the purpose of carrying a low-cost hotspot analysis ofa the pavement thatframework are presentfor on the an urban road
Summary
Road authorities worldwide are facing increasingly daunting challenges with regards to Pavement Maintenance and rehabilitation (M&R) programs. This is largely due to deficient budgets which have faced even further reductions over the last few years [1]. The accuracies in some instances are solve complex and different problems across varying research fields [15] This has been due to the considered even than those of human abilities asThe demonstrated the ImageNet largenow scale increases in better the accuracy of these methodologies. Accuracies inbysome instances are even better than those of human abilities as demonstrated by the ImageNet large scale in visualconsidered recognition challenge [16] in which the Human benchmark for recognizing objects was beaten visual recognitiondeep challenge [16] in which the Human benchmark for recognizing objects was beaten
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