DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
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In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with specialized encoder-decoders for thermal and color modalities and a shared decoder for final segmentation. We combine two strategies for feature fusion: confidence weighting and correlation weighting. We report state-of-the-art mean IoU results on the MF dataset [1].
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DooDLeNet, using a ResNet101 backbone, achieves an mIoU of 57.3 on the MSSSD/MF dataset for RGB + Thermal semantic segmentation.
“| DooDLeNet [111] | ResNet101 [16] | 57.3 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [111]
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Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
- Authors
- Frigo, O., Martin-Gaffe, L., & Wacongne, C.
- Affiliations
- AnotherBrain,Paris,France
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 3020-3028
- OpenAlex
- W4292828872
- Cited by
- 32
- References
- 27
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 11 · 2023: 6 · 2022: 3
- FWCI
- 2.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Commission (826131)
- Electronic Components and Systems for European Leadership (826131)
- 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
- feature fusion, DeepLab, encoder-decoder architecture, confidence weighting, driver perception
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