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arXiv · 2204.10266

DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation

Oriel Frigo·Lucien Martin-Gaffé·Catherine Wacongne
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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.

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Oriel Frigo, Lucien Martin-Gaffé, Catherine Wacongne. 2022-04-21. DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation. https://arxiv.org/abs/2204.10266

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