  Road transport infrastructure is critical for safe, fast, economical, and
reliable mobility within the whole country that is conducive to a productive
society. However, roads tend to deteriorate over time due to natural causes in
the environment and repeated traffic loads. Pavement Distress (PD) detection is
essential in monitoring the current conditions of the public roads to enable
targeted rehabilitation and preventive maintenance. Nonetheless, distress
detection surveys are still done via manual inspection for developing countries
such as the Philippines. This study proposed the use of deep learning for two
ways of recording pavement distresses from 2D RGB images - detection and
segmentation. YOLOv4 is used for pavement distress detection while DeepLabv3 is
employed for pavement distress segmentation on a small dataset of pavement
images in the Philippines. This study aims to provide a basis to potentially
spark solutions in building a cheap, scalable, and automated end-to-end
solution for PD detection in the country.
