Next Article in Journal
Aerodynamic Testing of Helicopter Side Intake Retrofit Modifications
Next Article in Special Issue
Experimental Investigation of the Wake and the Wingtip Vortices of a UAV Model
Previous Article in Journal / Special Issue
Nonlinear Model Predictive Control for Unmanned Aerial Vehicles
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
Aerospace 2017, 4(2), 32;

Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 210016, China
Author to whom correspondence should be addressed.
Academic Editor: Michael Wing
Received: 24 April 2017 / Revised: 25 May 2017 / Accepted: 12 June 2017 / Published: 20 June 2017
(This article belongs to the Collection Unmanned Aerial Systems)
Full-Text   |   PDF [8834 KB, uploaded 20 June 2017]   |  


We propose a robust approach to detecting and tracking moving objects for a naval unmanned aircraft system (UAS) landing on an aircraft carrier. The frame difference algorithm follows a simple principle to achieve real-time tracking, whereas Faster Region-Convolutional Neural Network (R-CNN) performs highly precise detection and tracking characteristics. We thus combine Faster R-CNN with the frame difference method, which is demonstrated to exhibit robust and real-time detection and tracking performance. In our UAS landing experiments, two cameras placed on both sides of the runway are used to capture the moving UAS. When the UAS is captured, the joint algorithm uses frame difference to detect the moving target (UAS). As soon as the Faster R-CNN algorithm accurately detects the UAS, the detection priority is given to Faster R-CNN. In this manner, we also perform motion segmentation and object detection in the presence of changes in the environment, such as illumination variation or “walking persons”. By combining the 2 algorithms we can accurately detect and track objects with a tracking accuracy rate of up to 99% and a frame per second of up to 40 Hz. Thus, a solid foundation is laid for subsequent landing guidance. View Full-Text
Keywords: deep learning; Faster R-CNN; UAS landing; object detection deep learning; Faster R-CNN; UAS landing; object detection

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yang, Y.; Gong, H.; Wang, X.; Sun, P. Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing. Aerospace 2017, 4, 32.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Aerospace EISSN 2226-4310 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top