Next Article in Journal
A Performance Study of Massive MIMO Heterogeneous Networks with Ricean/Rayleigh Fading
Next Article in Special Issue
Harmonic Extended State Observer Based Anti-Swing Attitude Control for Quadrotor with Slung Load
Previous Article in Journal
Implementation of a Cost-Effective Didactic Prototype for the Acquisition of Biomedical Signals
Previous Article in Special Issue
Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Electronics 2018, 7(6), 78; https://doi.org/10.3390/electronics7060078

Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network

1
,
1,2,* and 3,*
1
SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
2
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
3
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
*
Authors to whom correspondence should be addressed.
Received: 3 April 2018 / Revised: 16 May 2018 / Accepted: 19 May 2018 / Published: 23 May 2018
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
Full-Text   |   PDF [2694 KB, uploaded 23 May 2018]   |  

Abstract

An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to make full use of them—especially in ground vehicle detection based on aerial imagery–has aroused wide academic concern. However, due to the aerial imagery’s low-resolution and the vehicle detection’s complexity, how to extract remarkable features and handle pose variations, view changes as well as surrounding radiation remains a challenge. In fact, these typical abstract features extracted by convolutional neural networks are more recognizable than the engineering features, and those complex conditions involved can be learned and memorized before. In this paper, a novel approach towards ground vehicle detection in aerial infrared images based on a convolutional neural network is proposed. The UAV and the infrared sensor used in this application are firstly introduced. Then, a novel aerial moving platform is built and an aerial infrared vehicle dataset is unprecedentedly constructed. We publicly release this dataset (NPU_CS_UAV_IR_DATA), which can be used for the following research in this field. Next, an end-to-end convolutional neural network is built. With large amounts of recognized features being iteratively learned, a real-time ground vehicle model is constructed. It has the unique ability to detect both the stationary vehicles and moving vehicles in real urban environments. We evaluate the proposed algorithm on some low–resolution aerial infrared images. Experiments on the NPU_CS_UAV_IR_DATA dataset demonstrate that the proposed method is effective and efficient to recognize the ground vehicles. Moreover it can accomplish the task in real-time while achieving superior performances in leak and false alarm ratio. View Full-Text
Keywords: aerial infrared imagery; real-time ground vehicle detection; convolutional neural network; unmanned aerial vehicle aerial infrared imagery; real-time ground vehicle detection; convolutional neural network; unmanned aerial vehicle
Figures

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, X.; Yang, T.; Li, J. Real-Time Ground Vehicle Detection in Aerial Infrared Imagery Based on Convolutional Neural Network. Electronics 2018, 7, 78.

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

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top