Skip to main content
Springer Nature Link
Log in

3D Reconstruction of flame temperature field based on lightweight residual network with spatial attention mechanism

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Flame temperature field measurement has always been a key topic in combustion research, which is of great significance for combustion state diagnosis and fuel combustion optimization. Deep learning technology exploits its superior nonlinear ability to rapidly reconstruct the three-dimensional (3D) temperature field of flames from flame light-field images. However, existing algorithms have problems with complex networks, poor noise resistance and low reconstruction accuracy. This paper proposes a lightweight 3D flame temperature field reconstruction algorithm based on an improved MobileNet that combines residuals and spatial attention mechanisms. This method basically achieves a balance between low complexity and high accuracy and has good noise resistance performance. Simulation results show that the average relative error of temperature field reconstruction on the noise-free unimodal flame dataset is only 0.022%, and its computational complexity is only one-tenth of the existing CNN. The noise simulation experiment shows that this method has good noise resistance performance. The maximum relative error and the average relative error at Gaussian white noise standard deviation σ = 0.15 are only 2.91% and 0.27%, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The dataset and codes utilized in this paper are currently not publicly accessible; however, interested parties may acquire them by contacting the authors through reasona-ble requests.

References

  1. Liu, Y.: Study of light field sectioning pyrometry for three-dimensional flame temperature measurement. N Univ. 2 (2020). https://doi.org/10.27014/d.cnki.gdnau.2020.003115

  2. Wang, C.: 3-D temperature measurement of flame based on multi-camera light field imaging technique. N Univ. 6 (2019). https://doi.org/10.27014/d.cnki.gdnau.2019.001655

  3. Lyu, Z., et al.: A comprehensive investigation of LSTM-CNN deep learning model for fast detection of combustion instability. Fuel. 303, 121300 (2021). https://doi.org/10.1016/j.fuel.2021.121300

    Article  Google Scholar 

  4. Qin, Li, et al.: A flame imaging-based online deep learning model for predicting NOx emissions from an oxy-biomass combustion process. IEEE Trans. Instrum. Meas. 71, 1–11 (2021). https://doi.org/10.1109/TIM.2021.3132998

    Article  Google Scholar 

  5. Hu, Y., Tang, J., Xu, Y., et al.: EL-DenseNet: A novel method for identifying the flame state of converter steelmaking based on dense convolutional neural networks[J]. Signal. Image Video Process. 1–13 (2024). https://doi.org/10.1007/s11760-024-03011-9

  6. Yu, L.: Reconstruction of turbulent flame temperature field based on Radiation Inversion. N N Electr. N Univ. (4) (2019)

  7. He, J.: Research on measurement and three-dimensional reconstruction of the flame temperature field based on image detection. Zhejiang Sci-Tech Univ. (12) (2013)

  8. Qi, Q., et al.: Approach to optimize the sampling of multi-light field camera system based on feature rays selection. Chem. Ind. Eng. Prog. 40(12), 6581–6589 (2021). https://doi.org/10.16085/j.issn.1000-6613.2021-1326

    Article  Google Scholar 

  9. Sun, J., et al.: Three-dimensional temperature measurement of the Flame using the light field Camera with a multiple focus microlens array. J. Eng. Thermophys. 38(10), 2164–2170 (2017)

    Google Scholar 

  10. Huang, X., et al.: Temperature field reconstruction of scattering flame based on light-field imaging. J. Beijing Univ. Aeronaut. Astronaut. 46(5), 952–959 (2020). https://doi.org/10.13700/j.bh.1001-5965.2019.0337

    Article  Google Scholar 

  11. Xie, Z., et al.: Comparative studies of Tikhonov regularization and truncated singular value decomposition in the three-dimensional flame temperature field reconstruction. N Phys. Sin. 64(24), 21–28 (2015). https://doi.org/10.7498/aps.64.240201

    Article  Google Scholar 

  12. Huang, Q., et al.: Study on three-dimensional flame temperature distribution reconstruction based on truncated singular value decomposition. N Phys. Sin. 11, 6742–6748 (2007)

    Article  Google Scholar 

  13. Ding, J., et al.: Research on nonlinear optimization algorithm for furnace flame temperature field reconstruction. Proc. CSEE. 02, 140–143 (2003). https://doi.org/10.13334/j.02588013.pcsee.2003.02.029

    Article  Google Scholar 

  14. Huang, J., et al.: Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning. J. Quant. Spectrosc. Radiat. Transf. 218, 187–193 (2018). https://doi.org/10.1016/j.jqsrt.2018.07.011

    Article  Google Scholar 

  15. Li, Z., Lou, C.: Reconstruction of temperature and soot volume fraction distribution of Ethylene Laminar Diffusion Flame based on deep learning. J. Combust. Sci. Technol. 28(02), 198–205 (2022). https://doi.org/10.11715/rskxjs.R202202013

    Article  Google Scholar 

  16. Zhang, J.: Three-dimensional temperature field reconstruction of flame based on deep learning and light field imaging. J. N Univ. 51(6) (2021). https://doi.org/10.27014/d.cnki.gdnau.2021.004361

  17. Shang, J.: 3D flame real field extraction based on deep learning. N N Electr. N Univ. https://doi.org/10.27140/d.cnki.ghbbu.2020.000374

  18. Si, J., et al.: Tunable diode laser absorption tomography based on hierarchical discretization and residual network. J. Electron. Inf. Technol. 44(07), 2539–2546 (2022). https://doi.org/10.11999/JEIT210160

    Article  Google Scholar 

  19. Tao, Y., Cai, W., Liu, Y.: Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics. Rev. Ent Instrum. 89(4), 43101 (2018). https://doi.org/10.1063/1.5016403

    Article  Google Scholar 

  20. Fu, G.: Research on absorption spectroscopy tomography method of combustion field based on deep learning. Yanshan Univ. https://doi.org/10.27440/d.cnki.gysdu.2022.001475

  21. Eko, P., et al.: Combining MobileNetV1 and depthwise separable convolution bottleneck with expansion for classifying the freshness of fish eyes. Inf. Process. Agric. 9(4), 485–496 (2022). https://doi.org/10.1016/J.INPA.2022.01.002

    Article  Google Scholar 

  22. Lu, X., Li, A.: Depthwise Separable Convolutional SAR Target Recognition Embedded in Attention Mechanism. Radio Eng. 1–10 (2023). http://kns.cnki.net/kcms/detail/13.1097.TN.20230906.1342.028.html

  23. Cheng, B., et al.: Prediction of remaining service life of aero-engines based on residual NLSTM network and attention mechanism. J. Aerosp. N A. 38(05), 1176–1184 (2023). https://doi.org/10.13224/j.cnki.jasp.20210728

    Article  Google Scholar 

  24. Zhao, X., Song, Z.: Super-resolution Reconstruction of deep residual network with Multi-level skip connections. J. Electron. Inf. Technol. 41(10), 2501–2508 (2019). https://doi.org/10.11999/JEIT190036

    Article  Google Scholar 

  25. Lan, X.: Research on the error of different optimization algorithms for neural network construction model. Technol. Innov. Appl. 22, 18–19 (2020)

    Google Scholar 

  26. Wen, J.: Comparison of the effects of MSE and MAE on Machine Learning Performance optimization. Inf. Comput. 15, 42–43 (2018)

    Google Scholar 

  27. Sun, J.: Research on three-dimensional temperature field measurement method of flame based on light field imaging. N Univ. (2018)

  28. Yang, J.: Research on lightweight of object detection method based on deep learning. Univ. Chin. Acad. Sci. (2022)

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant No.51874264, Grant No.52076200).

Author information

Authors and Affiliations

  1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Liang Shan, Jian Sun & Bo Hong

  2. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou, 310018, China

    Ming Kong

Authors
  1. Liang Shan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jian Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bo Hong
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ming Kong
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

LS and JS conducted preliminary research and designed the method. BH and MK provided technical guidance. LS and JS carried out experi ments. JS wrote the manuscript. LS and JS revised the manuscript. All authors reviewed the final version of the manuscript.

Corresponding author

Correspondence to Ming Kong.

Ethics declarations

Ethical approval

Not applicable.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shan, L., Sun, J., Hong, B. et al. 3D Reconstruction of flame temperature field based on lightweight residual network with spatial attention mechanism. SIViP 18, 6661–6670 (2024). https://doi.org/10.1007/s11760-024-03342-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-024-03342-7

Keywords