  Accurate automated detection of road pavement distresses is critical for the
timely identification and repair of potentially accident-inducing road hazards
such as potholes and other surface-level asphalt cracks. Deployment of such a
system would be further advantageous in low-resource environments where lack of
government funding for infrastructure maintenance typically entails heightened
risks of potentially fatal vehicular road accidents as a result of inadequate
and infrequent manual inspection of road systems for road hazards. To remedy
this, a recent research initiative organized by the Institute of Electrical and
Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage
Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated
image dataset of various road distresses calling upon academic and other
researchers to submit innovative deep learning-based solutions to these road
hazard detection problems. Making use of this dataset, we propose a supervised
object detection approach leveraging You Only Look Once ("YOLO") and the Faster
R-CNN frameworks to detect and classify road distresses in real-time via a
vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score
experimental results ranking in the top 5 of 121 teams that entered this
challenge as of December 2021.
