Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection

Abstract

Road infrastructure networks are crucial in facilitating smart mobility, as indicated by the emergence of innovative transportation concepts that offer improved efficiency and environmental sustainability. This study seeks to review the literature regarding road pavement condition assessment performance improvement tools which utilize various computer vision and photogrammetry tools aided by machine learning algorithms towards mitigating challenges encountered and promoting smart transportation trends. A comprehensive search of available literature was conducted, and relevant studies were analyzed to identify computer vision and photogrammetry tools used, learning-based algorithms deployed and contribution to the improvement of road infrastructure to aid smart transportation. The review considered emerging challenges of the techniques, identified research gaps and explored the potentials of the techniques as it relates to aiding wider acceptance of the implementation of autonomous vehicles and smart transportation The study found gaps in knowledge relating to the computer vision (CV) and photogrammetry tools standardization of evaluation parameters, the applicability of the models for real-time assessment and implications regarding the adoption of autonomous vehicles and smart transportation which were not sufficiently considered in the previous cited literature. Future research areas were highlighted and its implication regarding the promotion of smart transportation.