Cited by 170Advanced Neural Network ApplicationsAdvanced Image Fusion TechniquesRemote-Sensing Image Classification

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FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

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Deng, Fuqin, Feng, Hua, Liang, Mingjian, Wang, Hongmin, Yang, Yong, Gao, Yuan, Chen, Junfeng, Hu, Junjie, Guo, Xiyue, Lam, Tin Lun·2021·2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI 10.1109/iros51168.2021.9636084

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Abstract

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.

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Authors: Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., & Lam, T. L.
Affiliations: Chinese University of Hong Kong, Shenzhen (CN)
Wuyi University (CN)
ORCID: Deng, Fuqin
Wang, Hongmin
Yang, Yong
Gao, Yuan
Chen, Junfeng
Hu, Junjie
Guo, Xiyue
Lam, Tin Lun
Venue: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher: IEEE
Published: 2021-09-27
Pages: 4467-4473
ISSN: 2153-0858 (ISSN-L)
DOI: 10.1109/iros51168.2021.9636084
OpenAlex: W3206439726
Cited by: 170
References: 31
Citations by year: 2026: 24 · 2025: 56 · 2024: 40 · 2023: 32 · 2022: 18
FWCI: 7.36
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: cc-by
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/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
arXiv (Cornell University) (cc-by)
Funders: Wuyi University
Shenzhen Peacock Plan
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 › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Keywords: thermal infrared imaging, attention mechanism, feature enhancement, multi-level feature extraction, real-time inference, urban scene segmentation

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FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

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