The condition of road infrastructure is dynamic, constantly changing, and vital to ensure the safety and economic efficiency of an urban area, but current manual inspection techniques are subjective, labour intensive and time-consuming. This paper introduces an Automated Pothole Detection and Severity Mapping system that proposes using real time measurement to address early objective measurement of the pothole condition of the road surface. This monitoring approach takes a sensor-fusion point of view by incorporating visual monitoring via Computer Vision (CV) with physical monitoring via Inertial Measurement Unit (IMU). It applies a model that can detect fast speed of object moving in order to detect deformation in the surface and a model that applies a synchronized Accelerometer to detect vertical movement to determine the depth of the pothole. The hazards that are detected are classified into three different levels of severity (Minor, Moderate and Critical) and are automatically uploaded to a Geographic Information System (GIS) dashboard using GPS co-ordinates. Results from experimentation showed that false positives (like shadows or oil spills) can be greatly reduced by using visual and physical data simultaneously as against vision only. An automated, scalable solution to assist municipalities prioritize road repair, more effectively utilize resources, and enhance commuter safety.
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