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

被引：16

作者：

Frigo, Oriel ^{[1
]}

Martin-Gaffe, Lucien ^{[1
]}

Wacongne, Catherine ^{[1
]}

机构：

[1] AnotherBrain, Paris, France

来源：

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2022 | 2022年

基金：

欧盟地平线“2020”;

关键词：

D O I：

10.1109/CVPRW56347.2022.00341

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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].

引用

页码：3020 / 3028

页数：9

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