Vehicle-detection-based traffic density estimation at road intersections

Huu-Huy Ngo

Abstract


Traffic congestion is currently affecting people's lives, which is a big and very urgent problem that needs to be solved. Therefore, this study will present a model to estimate traffic density at intersections based on vehicle detection. This study will determine the traffic density at intersections and the times with the highest vehicle. The findings from this study will assist in proposing solutions to optimize traffic flow and reduce traffic congestion. This study created a labeled database (Veh5) which includes 2,500 images of 5 common types of road vehicles. First, the system extracts consecutive frames from the input video to perform processing on those separate frames. Next, the YOLOv8 model is used to detect objects on each frame. This model was trained on Veh5 dataset with high accuracy, reaching mAP50 and mAP50-95 values of 0.994 and 0.915 respectively. Additionally, this study also assesses traffic density at some intersections in Thai Nguyen province, Vietnam, through the C-ThaiNguyen app. Experimental results demonstrate the effectiveness of this model.

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References


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