Computer Vision-based road surveillance system using autonomous drones and sensor fusion

This paper shows an innovative approach to road monitoring by integrating autonomous drones and sensor fusion within a computer vision-based system. By employing different sets of algorithms, drones equipped with cameras, GPS, and ultrasonic distance sensors can efficiently detect and geolocate road damages, providing crucial data for maintenance and infrastructure management. The system’s key components include automated planning, autonomous flight capabilities, object detection, and sensor fusion techniques, enhancing scalability and adaptability. The main objective is to use context information to compute a flying plan where the subsequent detection of defects allows us to expand and enhance GIS data. The implementation of such a system holds significant potential for improving road safety and optimizing maintenance costs, marking a notable advancement in the convergence of autonomous technologies, sensor fusion, and computer vision for effective road surveillance.