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

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arxiv 2204.10266 v1 pith:DIJ4QSB5 submitted 2022-04-21 cs.LG cs.CVeess.IV

Oriel Frigo , Lucien Martin-Gaffé , Catherine Wacongne This is my paper · ORCID
classification cs.LGcs.CVeess.IV
keywords featurefusionsegmentationdeeplabdoodlenetdoublesemanticthermal
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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.

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  1. FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management

    cs.CV 2024-12 conditional novelty 7.0 of 10

    FLAME 3 provides the first aerial radiometric thermal wildfire image dataset with per-pixel temperature TIFFs and nadir thermal plots, plus a pipeline and benchmark.

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