论文标题
doodlenet：Double DeepLab增强功能融合用于热色语义分割
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation
论文作者
论文摘要
在本文中，我们提出了一种新的方法，用于在RGB和LWIR热图像之间进行特征融合，以进行语义分割以驱动感知。我们提出了Doodlenet，这是一种双层底线架构，具有专门的编码器描述器，用于热和颜色模式，以及用于最终分割的共享解码器。我们结合了特征融合的两种策略：置信度加权和相关加权。我们在MF数据集上报告最先进的IOU结果。
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.
