A Novel Pothole Detection Model Based on YOLO Algorithm for VANET - S., Kamalakannan; S., Navaneethan; Deshmukh , Yogesh S.; V., Sujay; Sundari M., Sivakami; Ramalingam , Venkadeshan; D., Jagadeesan; C., Venkatesh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The well-maintained highways are essential to the nation's economy because roads serve as the primary means of transport. Locating the potholes is crucial for avoiding accidents and vehicle damage caused by driver distress, as well as minimizing the consumption of fuel. This paper offers a straightforward method for identifying potholes in that regard and preventing collisions and supporting drivers. Deep learning technology is used to detect potholes. Raspberry Pi is used as the controlling device. Using Wi-Fi, the device locates potholes geographically and sends that information to the appropriate authorities for repair. The first action in solving this problem is to design a device that can detect potholes on the road surface continually, warning the driver so drivers can avoid them. The device utilizes the Global Positioning System (GPS) to find the pothole's position. This database is transferred to the cloud by connecting the system to Wi-Fi or 4G technology. The experiments are carried out with a picture database with potholes in different road conditions, different lighting conditions, and real-time video recorded by a speeding vehicle. The You Only Look Once (YOLO) architecture produces a very fast inference time.