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Accurate automated detection of road pavement distresses is critical for thetimely identification and repair of potentially accident-inducing road hazardssuch as potholes and other surface-level asphalt cracks. Deployment of such asystem would be further advantageous in low-resource environments where lack ofgovernment funding for infrastructure maintenance typically entails heightenedrisks of potentially fatal vehicular road accidents as a result of inadequateand infrequent manual inspection of road systems for road hazards. To remedythis, a recent research initiative organized by the Institute of Electrical andElectronics Engineers ("IEEE") as part of their 2020 Global Road DamageDetection ("GRDC") Challenge published in May 2020 a novel 21,041 annotatedimage dataset of various road distresses calling upon academic and otherresearchers to submit innovative deep learning-based solutions to these roadhazard detection problems. Making use of this dataset, we propose a supervisedobject detection approach leveraging You Only Look Once ("YOLO") and the FasterR-CNN frameworks to detect and classify road distresses in real-time via avehicle dashboard-mounted smartphone camera, producing 0.68 F1-scoreexperimental results ranking in the top 5 of 121 teams that entered thischallenge as of December 2021.

A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management