Next Article in Journal
A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects
Next Article in Special Issue
An Indoor Robust Localization Algorithm Based on Data Association Technique
Previous Article in Journal
Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning
Previous Article in Special Issue
Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs)
Article

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

1
Faculty of Automatic Control and Computers, University Politehnica of Bucharest, RO-060042 Bucharest, Romania
2
National Institute for Research and Development in Informatics, RO-011455 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(22), 6485; https://doi.org/10.3390/s20226485
Received: 24 September 2020 / Revised: 11 November 2020 / Accepted: 11 November 2020 / Published: 13 November 2020
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. View Full-Text
Keywords: object detection model; satellite images; vehicle detection; smart city object detection model; satellite images; vehicle detection; smart city
Show Figures

Figure 1

MDPI and ACS Style

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

AMA Style

Stuparu D-G, Ciobanu R-I, Dobre C. Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors. 2020; 20(22):6485. https://doi.org/10.3390/s20226485

Chicago/Turabian Style

Stuparu, Delia-Georgiana, Radu-Ioan Ciobanu, and Ciprian Dobre. 2020. "Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model" Sensors 20, no. 22: 6485. https://doi.org/10.3390/s20226485

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop