Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways - Bastos, Mateus Eloi da Silva; Freitas, Vitor Yeso Fidelis; de Menezes, Richardson Santiago Teles; Maia, Helton (Anais do Seminário Integrado de Software e Hardware (SEMISH), 2020)
In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.