Road Anomaly Detection

Please Cited paper. A fusion approach of YOLOv8 and CNN-Transformer for End-to-End Road anomaly detection
DOI: https://doi.org/10.1038/s41598-025-29718-4
Abstract:
Surveillance cameras are common in both the private and public sectors for security and monitoring, and closed-circuit television (CCTV) systems are used for surveillance, generating large amounts of video data that cannot be manually monitored 24/7. The traditional approach to analysis is time-consuming and inefficient, and there is a growing need for automated surveillance systems that can recognize and classify anomalies. The research area that has been the most challenging to solve is AD systems that detect anomalies in data that is not structured according to the normal patterns. RNNs are slow and have difficulty identifying anomalies in the road that occur in multiple frames at the same time, whereas CNNs are limited in extracting temporal features from objects and generally disregard the background noise in video frames. In this study, a new framework for background removal is presented that removes the irrelevant background elements during object recognition.