DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
Oriel Frigo, Lucien Martin-Gaffé, Catherine Wacongne
Abstract
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.
Results
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Semantic Segmentation | MFN Dataset | mIOU | 57.3 | DooDLeNet |
| Scene Segmentation | MFN Dataset | mIOU | 57.3 | DooDLeNet |
| 2D Object Detection | MFN Dataset | mIOU | 57.3 | DooDLeNet |
| 10-shot image generation | MFN Dataset | mIOU | 57.3 | DooDLeNet |
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