Cited by 550Advanced Neural Network ApplicationsAdvanced Image Fusion TechniquesAutonomous Vehicle Technology and Safety

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RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes

Sun, Yuxiang, Zuo, Weixun, Liu, Ming·2019·IEEE Robotics and Automation Letters

DOI 10.1109/lra.2019.2904733

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Abstract

Semantic segmentation is a fundamental capability for autonomous vehicles. With the advancements of deep learning technologies, many effective semantic segmentation networks have been proposed in recent years. However, most of them are designed using RGB images from visible cameras. The quality of RGB images is prone to be degraded under unsatisfied lighting conditions, such as darkness and glares of oncoming headlights, which imposes critical challenges for the networks that use only RGB images. Different from visible cameras, thermal imaging cameras generate images using thermal radiations. They are able to see under various lighting conditions. In order to enable robust and accurate semantic segmentation for autonomous vehicles, we take the advantage of thermal images and fuse both the RGB and thermal information in a novel deep neural network. The main innovation of this letter is the architecture of the proposed network. We adopt the encoder–decoder design concept. ResNet is employed for feature extraction and a new decoder is developed to restore the feature map resolution. The experimental results prove that our network outperforms the state of the arts.

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Authors: Sun, Y., Zuo, W., & Liu, M.
Affiliations: Hong Kong University of Science and Technology (HK)
ORCID: Sun, Yuxiang
Zuo, Weixun
Liu, Ming
Venue: IEEE Robotics and Automation Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-07
Volume: 4
Issue: 3
Pages: 2576-2583
ISSN: 2377-3766 (ISSN-L)
2377-3774
DOI: 10.1109/lra.2019.2904733
OpenAlex: W2921749009
Cited by: 546
References: 30
Citations by year: 2026: 87 · 2025: 127 · 2024: 102 · 2023: 94 · 2022: 51 · 2021: 45 · 2020: 31 · 2019: 9
FWCI: 15.22
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Robotics and Automation Letters (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders: Shenzhen Science, Technology, and Innovation Commission (JCYJ20160401100022706)
National Natural Science Foundation of China (U1713211)
Hong Kong University of Science and Technology Project (IGN16EG12)
Hong Kong Research Grant Council (11210017, 21202816)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords: RGB-thermal fusion, semantic segmentation, urban scenes, autonomous vehicles, infrared thermography, encoder-decoder architecture, ResNet feature extraction, low-light conditions, headlight glare

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RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes

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