  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.
