Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark
<|reference_start|>Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark: 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.<|reference_end|>
@article{wang2024invisible,
    title={Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A
  New Benchmark},
    author={Jue Wang, Yuxiang Lin, Qi Zhao, Dong Luo, Shuaibao Chen, Wei Chen,
  Xiaojiang Peng},
    journal={arXiv preprint arXiv:2403.17712},
    year={2024},
    doi={10.1016/j.cviu.2024.104099},
    archivePrefix={arXiv},
    eprint={2403.17712},
    primaryClass={cs.CV}
}