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arXiv · 2403.17712

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
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Abstract

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 can be found at https://github.com/logic112358/RT-CAN.

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Jue Wang, Yuxiang Lin, Qi Zhao, Dong Luo, Shuaibao Chen, Wei Chen, Xiaojiang Peng. 2024-09-06. Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark. https://doi.org/10.1016/j.cviu.2024.104099

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