首页|Invisible gas detection: An RGB-thermal cross attention network and a new benchmark

Invisible gas detection: An RGB-thermal cross attention network and a new benchmark

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  • NETL
  • NSTL
  • Elsevier
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 CRT-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。

Gas detectionComputer visionRGB-ThermalGas-DB

Jue Wang、Yuxiang Lin、Qi Zhao、Dong Luo、Shuaibao Chen、Wei Chen、Xiaojiang Peng

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Southern University of Science and Technology, Shenzhen, 518055, China||Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

2024

10.1016/j.cviu.2024.104099

Computer vision and image understanding

Computer vision and image understanding

EISCI
ISSN：1077-3142
年,卷(期)：2024.248(Nov.)
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