Road Marking Sign Damage Detection and Classification Using Deep Learning and Fuzzy Method

Road marking signs are part of traffic signs on the road's surface. It can be found on almost every section of the road. It gives information about the road lanes and helps the driver understand the road ahead. Located on the road surface, road marking signs are more vulnerable to damage. Maintaining the condition of road marking signs is very challenging. It is a combination of environmental, logistical, and financial factors. Keeping the road marking signs clear and visible is essential for road safety. Faded or damaged road marking signs are difficult for the driver to see and identify, which can lead to missing or wrong information being captured. This research proposes a novel method to detect damaged road marking signs and classify the damage level using the combination of YOLOv9, VAE, image processing techniques, and fuzzy logic. The experiment results show that YOLOv9-M performs best in general road marking sign detection with 0.92 precision, 0.906 recall, 0.935 mAP@50, and 0.668 mAP@50:95. YOLOv9-M also excels in detecting the damaged road marking signs with 0.872 detection rate. The combination of VAE and image processing techniques shows promising results in detecting damaged road marking signs with 0.8395 precision, 0.8212 recall, and 0.7295 accuracy. The combination of anomaly score from the VAE with fuzzy logic is used to classify the damage level into three categories, which is more meaningful in understanding the road marking sign condition.