INTEGRATED REAL-TIME TRAFFIC SIGN MONITORING AND ANOMALY DETECTION

Abstract

Road signs are critical elements of road safety, but anomalies in these signs can lead to confusion and accidents. This work presents a multi-stage approach to detecting and rectifying anomalous road signs in the Indian context. Leveraging deep learning algorithms like YOLOv8 and innovative methodologies such as synthetic data generation and autoencoder-based anomaly detection, we aim to enhance road sign management systems' accuracy and efficacy. We collected data along various routes, trained YOLOv8 with both normal and synthetic anomalous signs, and employed SAM for segmentation. Our results demonstrate promising performance metrics, indicating the effectiveness of our approach. Future work involves expanding the scope to include more anomalies and intensive autoencoder training, ultimately contributing to global road safety and the future of autonomous vehicles.