Papers › DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
Oriel Frigo, Lucien Martin-Gaffé, Catherine Wacongne
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Thermal Image Segmentation | MFN Dataset | DooDLeNet | mIOU | 57.3 | #21 of 55 | Archive leaderboard | report |
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