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.

化学气体的泄漏及探测是一个重要的问题，本文提出了一种基于红外热像的计算机视觉检测技术以及辅助的多模态网络架构，通过整合来自RGB图像的纹理信息和来自热像的气体区域信息，在气体泄漏的检测方面取得了最新成果，优于现有模型的准确率、交叉并集和F2度量分别提高了4.86%、5.65%和4.88%。

隐形气体探测：一种基于RGB热力交叉关注网络的新基准