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.

翻译：各种化学气体在工业过程中的广泛使用，因其高毒性特性，亟需采取有效措施防止其在运输和储存过程中泄漏。基于热红外成像的计算机视觉检测技术为识别气体泄漏区域提供了直观方法。然而，热红外图像纹理匮乏以及开源数据集的缺失，使得高质量算法的研发面临挑战。本文提出RGB-热红外交叉注意力网络（RT-CAN），该网络采用RGB辅助双流网络架构，可同时整合RGB图像的纹理信息与热红外图像的气体区域信息。此外，为促进不可见气体检测研究，我们构建了Gas-DB——涵盖约1300张精细标注RGB-热红外图像的开源气体检测数据库，包含八种不同采集场景。实验结果表明，本方法成功融合两种模态优势，在RGB-热红外方法中达到了最优性能（SOTA），在准确率、交并比（IoU）和F2指标上分别超过单流SOTA模型4.86%、5.65%和4.88%。代码与数据集将尽快公开发布。