Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A
  New Benchmark
Jue Wang Yuxiang Lin Qi Zhao Dong Luo Shuaibao Chen Wei Chen Xiaojiang Peng
The widespread use of various chemical gases in industrial processes
necessitates effective measures to prevent their leakage during transportation
and storage, given their high toxicity. Thermal infrared-based computer vision
detection techniques provide a straightforward approach to identify gas leakage
areas. However, the development of high-quality algorithms has been challenging
due to the low texture in thermal images and the lack of open-source datasets.
In this paper, we present the RGB-Thermal Cross Attention Network (RT-CAN),
which employs an RGB-assisted two-stream network architecture to integrate
texture information from RGB images and gas area information from thermal
images. Additionally, to facilitate the research of invisible gas detection, we
introduce Gas-DB, an extensive open-source gas detection database including
about 1.3K well-annotated RGB-thermal images with eight variant collection
scenes. Experimental results demonstrate that our method successfully leverages
the advantages of both modalities, achieving state-of-the-art (SOTA)
performance among RGB-thermal methods, surpassing single-stream SOTA models in
terms of accuracy, Intersection of Union (IoU), and F2 metrics by 4.86%, 5.65%,
and 4.88%, respectively. The code and data will be made available soon.