DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos

Li, Qingpeng and Mou, LiChao and Xu, Qizhi and Zhang, Yun and Zhu, Xiao Xiang (2019) R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos. IEEE Transactions on Geoscience and Remote Sensing, 57 (7), pp. 5028-5042. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2019.2895362. ISSN 0196-2892.

[img] PDF - Published version

Official URL: https://ieeexplore.ieee.org/document/8651485


Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R 3 -Net), to detect multioriented vehicles in aerial images and videos. More specially, R 3 -Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position-sensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0.

Item URL in elib:https://elib.dlr.de/134126/
Document Type:Article
Title:R3-Net: A Deep Network for Multioriented Vehicle Detection in Aerial Images and Videos
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Li, QingpengBeihang University, BeijingUNSPECIFIEDUNSPECIFIED
Xu, QizhiBeihang University, BeijingUNSPECIFIEDUNSPECIFIED
Zhang, YunUniversity of New BrunswickUNSPECIFIEDUNSPECIFIED
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 5028-5042
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Aerial images and videos, deep learning, multioriented detection, remote sensing, vehicle detection.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Mou, LiChao
Deposited On:17 Feb 2020 13:44
Last Modified:01 Apr 2021 03:00

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.