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Stuparu DG, Ciobanu RI, Dobre C. Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors (Basel). 2020;20(22)doi: 10.3390/s20226485.
Stuparu, D. G., Ciobanu, R. I., & Dobre, C. (2020). Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors (Basel, Switzerland), 20(22), . https://doi.org/10.3390/s20226485
Stuparu, Delia-Georgiana, et al. "Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model." Sensors (Basel, Switzerland) vol. 20,22 (2020). doi: https://doi.org/10.3390/s20226485
Stuparu DG, Ciobanu RI, Dobre C. Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors (Basel). 2020 Nov 13;20(22). doi: 10.3390/s20226485. PMID: 33202875; PMCID: PMC7696426.
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Sensors (Basel). 2020 Nov 13;20(22). doi: 10.3390/s20226485.

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

Sensors (Basel, Switzerland)

Delia-Georgiana Stuparu, Radu-Ioan Ciobanu, Ciprian Dobre

Affiliations

  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.

PMID: 33202875 PMCID: PMC7696426 DOI: 10.3390/s20226485

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.

Keywords: object detection model; satellite images; smart city; vehicle detection

References

  1. IEEE Trans Pattern Anal Mach Intell. 2020 Feb;42(2):318-327 - PubMed
  2. IEEE Trans Image Process. 2019 Jan;28(1):265-278 - PubMed

Publication Types

Grant support