A Novel YOLO-ARIA Approach for Real-Time Vehicle Detection and Classification in Urban Traffic

Abstract

The number of vehicles traveling between cities has increased significantly with the acceleration of urbanization. This has resulted in several traffic-related issues, including traffic congestion and the need to collect information on the number and variety of vehicles on the road. In this study, we propose you only look once (YOLO) artificial real-time intelligent analysis (ARIA) based intelligent traffic monitoring system. YOLO is an algorithm that is capable of detecting objects in images and recordings. We improved YOLO's feature extraction capabilities to improve its vehicle detection accuracy. In addition, we proposed detection models with C3X (convolution neural network) module in the backbone of YOLO. In our experiments, the proposed system attained 99,1% accuracy with 98,3% precision and 98,4% recall on datasets obtained from the CCTV monitoring portal of the semarang city government. In addition, the proposed system has a higher average precision than other vehicle detection and classification methods. Considering the current environmental conditions, the proposed system can classify vehicles in real-time. This makes it a valuable planning and traffic-management instrument.