Computer Vision Techniques for Estimation of Intensity, Speed and Density in Vehicular Flow

This paper describes the implementation and evaluation of several computer vision algorithms,which are YOLOv7,YOLOv5,YOLOv4, SSD MobileNet, EfficientDet D0 and SSD MobileNet LITE, with the objective of calculating the main magnitudes of vehicular flow such as intensity, density and speed. In addition, the use of the StrongSort object tracking algorithm wasexplored and the Deep Learnig frameworks TensorFlow, Darknet and PyTorch were employed. On the other hand, four experimentswere carried out in different lighting conditions, determining that the YOLOv5 and YOLOv7 architectures stand out as the most effective in both high and low lighting situations, outperforming the rest of the models. Finally, a highly efficient software prototype was developed to obtain the main magnitudes of vehicular flow, which represents a significant advance in the field of computer vision applied to traffic.