Next Article in Journal
A Microwave Microfluidic Sensor Based on a Dual-Mode Resonator for Dual-Sensing Applications
Next Article in Special Issue
Observing Spring and Fall Phenology in a Deciduous Forest with Aerial Drone Imagery
Previous Article in Journal
Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment
Previous Article in Special Issue
Designing and Testing a UAV Mapping System for Agricultural Field Surveying
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2720;

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks

1,2,3,* , 1
Institute of Optics and Electronics, Chinese Academy of Sciences, No. 1, Guangdian Avenue, Chengdu 610209, China
School of Optoelectronic Information, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, China
University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100039, China
Author to whom correspondence should be addressed.
Received: 13 October 2017 / Revised: 21 November 2017 / Accepted: 22 November 2017 / Published: 24 November 2017
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
Full-Text   |   PDF [7315 KB, uploaded 27 November 2017]   |  


Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed. View Full-Text
Keywords: vehicle detection; convolutional neural network; aerial image; deep learning vehicle detection; convolutional neural network; aerial image; deep learning

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

Zhong, J.; Lei, T.; Yao, G. Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks. Sensors 2017, 17, 2720.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top