Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
121 tokens/sec
GPT-4o
9 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark (2403.17712v2)

Published 26 Mar 2024 in cs.CV

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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. M. Meribout, Gas leak-detection and measurement systems: Prospects and future trends, IEEE Transactions on Instrumentation and Measurement 70 (2021) 1–13.
  2. S. R. Morrison, Mechanism of semiconductor gas sensor operation, Sensors and Actuators 11 (1987) 283–287.
  3. Spectral imaging applications: remote sensing, environmental monitoring, medicine, military operations, factory automation, and manufacturing, in: 25th AIPR Workshop: Emerging Applications of Computer Vision, volume 2962, SPIE, 1997, pp. 63–77.
  4. An infrared image enhancement algorithm for gas leak detecting based on gaussian filtering and adaptive histogram segmentation, in: 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), IEEE, 2021, pp. 359–363.
  5. An effective method for gas-leak area detection and gas identification with mid-infrared image, in: Photonics, volume 9, MDPI, 2022, p. 992.
  6. Machine vision for natural gas methane emissions detection using an infrared camera, Applied Energy 257 (2020) 113998.
  7. Videogasnet: Deep learning for natural gas methane leak classification using an infrared camera, Energy 238 (2022) 121516.
  8. Gas plume detection in infrared image using mask r-cnn with attention mechanism, in: AOPC 2019: AI in Optics and Photonics, volume 11342, SPIE, 2019, pp. 204–209.
  9. Mfnet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2017, pp. 5108–5115.
  10. Explicit attention-enhanced fusion for rgb-thermal perception tasks, IEEE Robotics and Automation Letters (2023).
  11. Multispectral fusion transformer network for rgb-thermal urban scene semantic segmentation, IEEE Geoscience and Remote Sensing Letters 19 (2022) 1–5.
  12. Feanet: Feature-enhanced attention network for rgb-thermal real-time semantic segmentation, in: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2021, pp. 4467–4473.
  13. The hitran2020 molecular spectroscopic database, Journal of quantitative spectroscopy and radiative transfer 277 (2022) 107949.
  14. J.-P. Tarel, N. Hautière, Fast visibility restoration from a single color or gray level image, in: 2009 IEEE 12th International Conference on Computer Vision, 2009, pp. 2201–2208. doi:10.1109/ICCV.2009.5459251.
  15. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  16. PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32, Curran Associates, Inc., 2019, pp. 8024–8035. URL: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
  17. Imagenet: A large-scale hierarchical image database, in: 2009 IEEE conference on computer vision and pattern recognition, Ieee, 2009, pp. 248–255.
  18. V-net: Fully convolutional neural networks for volumetric medical image segmentation, in: 2016 fourth international conference on 3D vision (3DV), Ieee, 2016, pp. 565–571.
  19. K. Yi, J. Wu, Probabilistic end-to-end noise correction for learning with noisy labels, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 7017–7025.
  20. Pytorch: An imperative style, high-performance deep learning library, Advances in neural information processing systems 32 (2019).
  21. Pyramid scene parsing network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2881–2890.
  22. Segformer: Simple and efficient design for semantic segmentation with transformers, Advances in Neural Information Processing Systems 34 (2021) 12077–12090.
  23. Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes, IEEE Robotics and Automation Letters 4 (2019) 2576–2583.
Citations (1)

Summary

We haven't generated a summary for this paper yet.