Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing
Open accessAbstract
RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed information in the prediction map and then embed the prior edge information in the feature maps. To effectively fuse the RGB and thermal information, we propose a multimodal fusion module that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features. For decoding, we use simple elementwise addition for cascaded feature fusion. Finally, to improve the parsing accuracy, we apply multitask deep supervision to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/ShaohuaDong2021/EGFNet.
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EGFNet, using a ResNet152 backbone, achieves an mIoU of 54.8 on the MSSSD/MF dataset for RGB + Thermal semantic segmentation.
“| EGFNet [114] | ResNet152 [16] | 54.8 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [114]
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Cited by
Sorted by citations
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
- Authors
- Zhou, W., Dong, S., Xu, C., & Qian, Y.
- Affiliations
- Zhejiang University of Science and Technology (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2022-06-28
- Volume
- 36
- Issue
- 3
- Pages
- 3571-3579
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4200635035
- Cited by
- 146
- References
- 40
- Citations by year
- 2026: 23 · 2025: 51 · 2024: 32 · 2023: 29 · 2022: 11
- FWCI
- 11.15
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61502429)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- multimodal fusion, boundary extraction, cross-modal feature fusion, semantic information extraction, infrared thermography, semantic segmentation
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