Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

by   Mingjian Liang, et al.

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as low light and fog. However, how to effectively fuse RGB images and thermal data remains an open challenge. Previous works involve naive fusion strategies such as merging them at the input, concatenating multi-modality features inside models, or applying attention to each data modality. These fusion strategies are straightforward yet insufficient. In this paper, we propose a novel fusion method named Explicit Attention-Enhanced Fusion (EAEF) that fully takes advantage of each type of data. Specifically, we consider the following cases: i) both RGB data and thermal data, ii) only one of the types of data, and iii) none of them generate discriminative features. EAEF uses one branch to enhance feature extraction for i) and iii) and the other branch to remedy insufficient representations for ii). The outputs of two branches are fused to form complementary features. As a result, the proposed fusion method outperforms state-of-the-art by 1.6% in mIoU on semantic segmentation, 3.1% in MAE on salient object detection, 2.3% in mAP on object detection, and 8.1% in MAE on crowd counting. The code is available at https://github.com/FreeformRobotics/EAEFNet.


page 1

page 2

page 3

page 4

page 5


Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection

RGB-thermal salient object detection (RGB-T SOD) aims to locate the comm...

TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting

In this paper, we propose a three-stream adaptive fusion network named T...

Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection

The RGB complementary metal-oxidesemiconductor (CMOS) sensor works withi...

Does Thermal data make the detection systems more reliable?

Deep learning-based detection networks have made remarkable progress in ...

HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

The High-Resolution Transformer (HRFormer) can maintain high-resolution ...

Real-time Human Detection in Fire Scenarios using Infrared and Thermal Imaging Fusion

Fire is considered one of the most serious threats to human lives which ...

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

In this paper we present a new approach for feature fusion between RGB a...

Please sign up or login with your details

Forgot password? Click here to reset