| Abstract |
In order to improve the traffic in large cities and to avoid congestion, advanced methods of detecting and predicting vehicle behaviour are needed. Such methods require complex information regarding the number of vehicles on the roads, their positions, directions, etc. One way to obtain this information is by analyzing overhead images collected by satellites or drones, and extracting information from them through intelligent machine learning models. Thus, in this paper we propose and present a one-stage object detection model for finding vehicles in satellite images using the RetinaNet architecture and the Cars Overhead With Context dataset. By analyzing the results obtained by the proposed model, we show that it has a very good vehicle detection accuracy and a very low detection time, which shows that it can be employed to successfully extract data from real-time satellite or drone data. |
