Autonomous Social Amenity, Pothole Detection and Reporting

Challenges faced by traffic management in many developing nations across the globe is a result of the poor quality of road infrastructure. Inadequate and defective road infrastructure are major contributors to this effect. A key element that poses significant risk to road safety and that leads to increased road management cost is the increasing number of potholes and inefficient detection and reporting approach. This study provides an in-depth exploration of the latest developments in pothole detection systems and examines various methodologies and technologies used in these systems, emphasizing the importance of real-time monitoring for proactive road maintenance. In addition, we proposed a system that addresses the persistent challenge of road infrastructure degradation by adopting cutting-edge technologies, including computer vision, machine learning, and sensor networks (such as ultrasonic and GPIO systems) that are used to identify and assess potholes on road surfaces. To achieve the aim of the study, the system adopts the use of RasberryPi 4 Model B for pothole detection and YOLOv4 for image processing.