YOLOV9 for Efficient Recognition of Road Marking Signs

Road marking signs are an important part of the road network. It gives information to the driver to help them understand the road conditions and improve driving safety. Fast and accurate road marking sign detection is a popular research topic. It is a basis for an efficient advanced driver assistance system (ADAS) and autonomous driving. In this research, we merged two datasets, The Taiwan Road Marking Sign Dataset (TRMSD) and the Taiwan Road Marking Sign Dataset at Night (TRMSDN), and relabeled the images to create the new dataset. The latest dataset provides more variation in lighting conditions, providing robustness to the trained model. YOLOv9, the latest YOLO, is trained and compared with YOLOv8 in the detection performance. The experiment shows that YOLOv9 performs better by achieving 0.921 precision, 0.925 recall, and 0.961 mAP50.