Urban-Trafficnet: Novel Stereo-Based Yolo Model for VehicleCategorization, Position, and Speed Analysis in Dense Urban Environment

Accurate vehicle classification and speed estimation are essential for effective traffic monitoring and management in urban environments. ‎This study presents a YOLOv5-based deep learning model integrated with an attenuation layer to enhance detection precision across diverse ‎vehicle categories. The system classifies stereo vision-based footage into seven major groups. A stereo camera setup captures live traffic ‎scenarios, allowing for depth estimation to determine object positions and velocities. The attenuation layer refines feature extraction by ‎reducing background noise, thereby increasing reliability in dense urban conditions. The YOLOv5 model achieved a high detection precision ‎of 99%, validating its effectiveness in multi-class vehicle recognition. The speed estimation method demonstrated high accuracy, with a low ‎margin of error of ±0.05, confirming its suitability for real-time applications. Integration of the attenuation layer significantly improved noise ‎resistance and overall model robustness in complex scenes. Thus, the proposed method enhances both detection accuracy and speed estimation capabilities, supporting advanced intelligent transportation systems for smarter urban traffic management‎.