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

Wang，Jue1,3; Lin，Yuxiang2; Zhao，Qi3; Luo，Dong3; Chen，Shuaibao3; Chen，Wei3; Peng，Xiaojiang2

2024-11-01

10.1016/j.cviu.2024.104099

Computer Vision and Image Understanding   影响因子和分区

1077-3142

1090-235X

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.

Computer vision Gas detection Gas-DB RGB-Thermal

SCI ; EI

英语

第一

COMPUTER SCIENCE

2-s2.0-85200597200

Scopus

1.Southern University of Science and Technology,Shenzhen,518055,China
2.College of Big Data and Internet,Shenzhen Technology University,Shenzhen,518118,China
3.Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China

Wang，Jue,Lin，Yuxiang,Zhao，Qi,et al. Invisible gas detection: An RGB-thermal cross attention network and a new benchmark[J]. Computer Vision and Image Understanding,2024,248.

Wang，Jue.,Lin，Yuxiang.,Zhao，Qi.,Luo，Dong.,Chen，Shuaibao.,...&Peng，Xiaojiang.(2024).Invisible gas detection: An RGB-thermal cross attention network and a new benchmark.Computer Vision and Image Understanding,248.

Wang，Jue,et al."Invisible gas detection: An RGB-thermal cross attention network and a new benchmark".Computer Vision and Image Understanding 248(2024).