Detection of Vehicle Density Based on YoloV7 with Images from Cusco, Peru

Urban sprawl and the growth of the vehicle fleet have the main problem of vehicular congestion. Nowadays, there are different ways to generate data from various sources, for instance, GPS devices and video surveillance cameras installed in strategic locations. These tools are used to address this problem. In this work, we apply the YOLOv7 model trained with the Argoverse-HD, and DS-ivic datasets (Dataset of images and videos of vehicles in the city of Cusco, Peru) to help intelligently manage vehicle congestion. Specifically, the research objective is to determine vehicle density for each type of vehicle and the automatic classification of vehicles in Cusco, Peru. For this purpose, the best Yolov7 model was determined by achieving the best performance with the DS-ivic dataset for the detection and vehicle density (car, bus, and truck). For this purpose, 2,866 images were labeled for the DS-ivic dataset. As a result, it was found that the YOLOv7 model with the DS-ivic dataset achieved a mAP50 of 57.89% compared to a mAP50 of 51.56% with the ArogverseHD dataset showing a significant enhancement. Likewise, these results were compared with the YOLOv7 trained model with the MS-COCO dataset, which achieved a mAP50 of 69.7% for the detection of vehicles detection.