Trigger-Based Pothole Detection, and Warning System with RQ and PHR Mapping

Pothole repair stands as a critical component of road maintenance, and the ongoing challenge faced by road management authorities involves the continuous monitoring of road surfaces. Currently, the detection of potholes relies on labor-intensive and time-consuming manual image/data processing. Furthermore, as traffic continues to grow exponentially, roads are becoming increasingly susceptible to damage within shorter time frames. However, there is an opportunity to address this challenge by leveraging computer vision, which can automate the visual inspection process using digital imaging to identify potholes from a sequence of images. The persistent issue of potholes and poor road quality threatens road safety and infrastructure integrity. This issue is underscored by alarming accident statistics, with thousands of fatalities and injuries reported annually. In this paper, a visual sensing pothole detection model, built upon YOLO V8, has been proposed. Additionally, road quality and PHR mapping via sensory data (accelerometer and gyrometer), which undergoes post-processing to provide quality observations for various vehicles and classify them, has been introduced. Leveraging this data, concerned officials were notified via the developed backend service at certain intervals about the quality and repairs required at specific locations. This streamlines the government’s efforts by deducing survey time, cost, and resource expenditure since the data is collected by vehicles on the road. Additionally, it notifies users of any upcoming potholes and road conditions ahead.