Enhanced Road Safety: Real-Time Pothole Detection Using YOLOv9 - Anbalagan, Daarshini; Hj Mohd, Mohd Norzali (Evolution in Electrical and Electronic Engineering, 2025)
The goal of this research is to develop a real time pothole detection system with the advanced YOLOv9 model which outperforms previous ones, such as YOLOv5 and YOLOv8. PGI and GELAN in YOLOv9 enhance the accuracy of the detection through programmable gradient information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN), which reduce data loss and improve the performance. The methodology involves training the YOLOv9 model on a large dataset of diverse images and video streams which cover a wide range of road conditions and lighting. The results show that the YOLOv9 model has a detection accuracy over 90% with real time processing speed of up to 31.76 frames per second (FPS). This study concludes that such advanced technology can be very useful in improving road safety by enabling faster repair and more effective maintenance, which in the end can help solve some of the major challenges in infrastruture management.