Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model

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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
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.
Description
Keywords
object detection model, satellite images, vehicle detection, smart city
Citation
Stuparu, D.-G.; Ciobanu, R.-I.; Dobre, C. Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors 2020, 20, 6485. https://doi.org/10.3390/s20226485
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