Skip to main content
SpringerLink
Log in

Combining Transformer and Convolutional Neural Network for Smoke Detection

  • Conference paper
  • First Online:
Digital Multimedia Communications (IFTC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1766))

  • 514 Accesses

Abstract

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model.

This work was supported in part by the National Science Foundation of China under Grant 62273011 and Grant 62076013; in part by the Beijing Natural Science Foundation under Grant JQ21014; in part by the Industry-University-Research Innovation Fund for Chinese University - Blue Point Distributed Intelligent Computing Project under 2021LDA03003; in part by the Ministry of Education of China under Grant 202102535002, Grant 202102535012; in part by the Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology under Grant LICO2022TB03.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Atkinson, R.: Atmospheric chemistry of VOCs and NOx. Atmos. Environ. 34(12–14), 2063–2101 (2000)

    Article  Google Scholar 

  2. Song, Y., et al.: Source apportionment of PM\(_{2.5}\) in Beijing by positive matrix factorization. Atmos. Environ. 40(8), 1526–1537 (2006)

    Article  Google Scholar 

  3. Motte, J., Alvarenga, R.A., Thybaut, J.W., Dewulf, J.: Quantification of the global and regional impacts of gas flaring on human health via spatial differentiation. Environ. Pollut. 291, 118213 (2021)

    Article  Google Scholar 

  4. Gu, K., Qiao, J., Lin, W.: Recurrent air quality predictor based on meteorology- and pollution-related factors. IEEE Trans. Industr. Inform. 14(9), 3946–3955 (2018)

    Article  Google Scholar 

  5. Soh, P.-W., Chang, J.-W., Huang, J.-W.: Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. IEEE Access 6, 38186–38199 (2018)

    Article  Google Scholar 

  6. Gu, K., Qiao, J., Li, X.: Highly efficient picture-based prediction of PM\(_{2.5}\) concentration. IEEE Trans. Ind. Electron. 66(4), 3176–3184 (2019)

    Article  Google Scholar 

  7. Lee, S.H., Lee, S., Song, B.C.: Vision transformer for small-size datasets. arXiv preprint arXiv:2112.13492 (2021)

  8. Gu, K., Liu, H., Xia, Z., Qiao, J., Lin, W., Thalmann, D.: PM\(_{2.5}\) monitoring: use information abundance measurement and wide and deep learning. IEEE Trans. Neural Netw. Learn. Syst. 32(10), 4278–4290 (2021)

    Article  Google Scholar 

  9. Yue, G., Gu, K., Qiao, J.: Effective and efficient photo-based concentration estimation. IEEE Trans. Instrum. Meas. 68(10), 396–3971 (2019)

    Article  Google Scholar 

  10. Gu, K., Liu, J., Shi, S., Xie, S., Shi, T., Qiao, J.: Self-organizing multi-channel deep learning system for river turbidity monitoring. IEEE Trans. Instrum. Meas. 71, 1–13 (2022)

    Google Scholar 

  11. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  12. Gu, K., Xia, Z., Qiao, J.: Stacked selective ensemble for PM\(_{2.5}\) forecast. IEEE Trans. Instrum. Meas. 69(3), 660–671 (2019)

    Article  Google Scholar 

  13. Gu, K., Xia, Z., Qiao, J., Lin, W.: Deep dual-channel neural network for image-based smoke detection. IEEE Trans. Multimedia 22(2), 311–323 (2020)

    Article  Google Scholar 

  14. Graham, B., et al.: Levit: a vision transformer in ConvNet’s clothing for faster inference. In: Proceedings IEEE International Conference on Computer Vision, pp. 12259–12269 (2021)

    Google Scholar 

  15. Gu, K., Zhang, Y., Qiao, J.: Ensemble meta learning for few-shot soot density recognition. IEEE Trans. Industr. Inform. 17(3), 2261–2270 (2021)

    Article  Google Scholar 

  16. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  17. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

  18. Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings IEEE/CVF Conference Computer Vision Pattern Recognition, pp. 6881–6890 (2021)

    Google Scholar 

  19. Chen, H., et al.: Pre-trained image processing transformer. In: Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12299–12310 (2021)

    Google Scholar 

  20. Ojo, J.A., Oladosu, J.A.: Video-based smoke detection algorithms: a chronological survey. In: Proceedings International Institute for Science, Technology and Education, pp. 2222–1719 (2014)

    Google Scholar 

  21. Gu, K., Li, L., Lu, H., Min, X., Lin, W.: A fast reliable image quality predictor by fusing micro-and macro-structures. IEEE Trans. Ind. Electron. 64(5), 3903–3912 (2017)

    Article  Google Scholar 

  22. Kaabi, R., Frizzi, S., Bouchouicha, M., Fnaiech, F., Moreau, E.: Video smoke detection review: State of the art of smoke detection in visible and IR range. In: Proceedings International Conference on Smart, Monitored and Controlled Cities, pp. 81–86 (2017)

    Google Scholar 

  23. Gu, K., Zhang, Y., Qiao, J.: Ensemble meta-learning for few-shot soot density recognition. IEEE Trans. Industr. Inform. 17(3), 2261–2270 (2020)

    Article  Google Scholar 

  24. Gaur, A., Singh, A., Kumar, A., Kumar, A., Kapoor, K.: Video flame and smoke based fire detection algorithms: a literature review. Fire Technol. 56(5), 1943–1980 (2020)

    Article  Google Scholar 

  25. Gu, K., Zhai, G., Yang, X., Zhang, W.: Hybrid no-reference quality metric for singly and multiply distorted images. IEEE Trans. Broadcast. 60(3), 555–567 (2014)

    Article  Google Scholar 

  26. Cui, Y., Dong, H., Zhou, E.: An early fire detection method based on smoke texture analysis and discrimination. In: Proceedings Congress Image Signal Processing, vol. 3, pp. 95–99 (2008)

    Google Scholar 

  27. Gubbi, J., Marusic, S., Palaniswami, M.: Smoke detection in video using wavelets and support vector machines. Fire Saf. J. 44(8), 1110–1115 (2009)

    Article  Google Scholar 

  28. Vidal-Calleja, T.A., Agammenoni, G.: Integrated probabilistic generative model for detecting smoke on visual images. In: Proceedings International Conference on Robotics and Automation, pp. 2183–2188 (2012)

    Google Scholar 

  29. Yuan, F.: Video-based smoke detection with histogram sequence of LBP and LBPV pyramids. Fire Saf. J. 46(3), 132–139 (2011)

    Article  Google Scholar 

  30. Yuan, F., Xia, X., Shi, J., Li, H., Li, G.: Non-linear dimensionality reduction and Gaussian process based classification method for smoke detection. IEEE Access 5, 6833–6841 (2017)

    Article  Google Scholar 

  31. Gu, K., Zhang, Y., Qiao, J.: Vision-based monitoring of flare soot. IEEE Trans. Instrum. Meas. 69(9), 7136–7145 (2020)

    Article  Google Scholar 

  32. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  33. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  34. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  35. Yin, Z., Wan, B., Yuan, F., Xia, X., Shi, J.: A deep normalization and convolutional neural network for image smoke detection. IEEE Access 5, 18429–18438 (2017)

    Article  Google Scholar 

  36. Gu, K., Xia, Z., Qiao, J., Lin, W.: Deep dual-channel neural network for image-based smoke detection. IEEE Trans. Multimedia 22(2), 311–323 (2019)

    Article  Google Scholar 

  37. Gu, K., Wang, S., Zhai, G., Ma, S., Lin, W.: Screen image quality assessment incorporating structural degradation measurement. In: Proceedings IEEE International Symposium on Circuits and Systems, pp. 125–128 (2015)

    Google Scholar 

  38. Yue, G., Hou, C., Gu, K., Zhou, T., Zhai, G.: Combining local and global measures for DIBR-synthesized image quality evaluation. IEEE Trans. Image Process. 28(4), 2075–2088 (2019)

    Article  MathSciNet  Google Scholar 

  39. Gu, K., Zhai, G., Yang, X., Zhang, W.: Deep learning network for blind image quality assessment. In: Proceedings IEEE International Conference on Image Processing, pp. 511–515 (2014)

    Google Scholar 

  40. Sun, W., Gu, K., Ma, S., Zhu, W., Liu, N., Zhai, G.: A large-scale compressed 360-degree spherical image database: from subjective quality evaluation to objective model comparison. In: Proceedings IEEE International Workshop on Multimedia Signal Processing, pp. 1–6 (2018)

    Google Scholar 

  41. Liu, Y., Qin, W., Liu, K., Zhang, F., Xiao, Z.: A dual convolution network using dark channel prior for image smoke classification. IEEE Access 7, 60697–60706 (2019)

    Article  Google Scholar 

  42. You, C., Li, Z., Li, M., Gao, Z., Li, W.: Db-net: dual attention network with bilinear pooling for fire-smoke image classification. J. Phys: Conf. Ser. 1631, 012054 (2020)

    Google Scholar 

  43. He, L., Gong, X., Zhang, S., Wang, L., Li, F.: Efficient attention based deep fusion CNN for smoke detection in fog environment. Neurocomputing 434, 224–238 (2021)

    Article  Google Scholar 

  44. Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. In: Advances in Neural Information Processing Systems, vol. 34, pp. 15908–15919 (2021)

    Google Scholar 

  45. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  46. Ali, A., et al.: XCiT: cross-covariance image transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 20014–20027 (2021)

    Google Scholar 

  47. Yuan, L., et al.: Tokens-to-token VIT: training vision transformers from scratch on ImageNet. In: Proceedings IEEE International Conference on Computer Vision, pp. 558–567 (2021)

    Google Scholar 

  48. Wu, H., et al.: CvT: introducing convolutions to vision transformers. In: Proceedings IEEE International Conference on Computer Vision, pp. 22–31 (2021)

    Google Scholar 

  49. Yuan, K., Guo, S., Liu, Z., Zhou, A., Yu, F., Wu, W.: Incorporating convolution designs into visual transformers. In: Proceedings IEEE International Conference on Computer Vision, pp. 579–588 (2021)

    Google Scholar 

  50. Li, Y., Zhang, K., Cao, J., Timofte, R., Van Gool, L.: Localvit: bringing locality to vision transformers. arXiv preprint arXiv:2104.05707 (2021)

  51. Gong, Y., Ma, X.: Multiple categories of visual smoke detection database. arXiv preprint arXiv:2208.00210 (2022)

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Yafei Gong, Xinkang Lian, Xuanchao Ma, Zhifang Xia & Chengxu Zhou

  2. Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing, China

    Yafei Gong, Xinkang Lian, Xuanchao Ma, Zhifang Xia & Chengxu Zhou

  3. Beijing Laboratory of Smart Environmental Protection, Beijing, China

    Yafei Gong, Xinkang Lian, Xuanchao Ma, Zhifang Xia & Chengxu Zhou

  4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, China

    Yafei Gong, Xinkang Lian, Xuanchao Ma, Zhifang Xia & Chengxu Zhou

  5. Beijing Artificial Intelligence Institute, Beijing, China

    Yafei Gong, Xinkang Lian, Xuanchao Ma, Zhifang Xia & Chengxu Zhou

  6. National Information Center, Beijing, China

    Zhifang Xia

  7. School of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, Liaoning, China

    Chengxu Zhou

  8. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China

    Chengxu Zhou

Authors
  1. Yafei Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinkang Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuanchao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhifang Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengxu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengxu Zhou .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  2. Shanghai Jiao Tong University, Shanghai, China

    Jun Zhou

  3. Shanghai Jiao Tong University, Shanghai, China

    Hua Yang

  4. Shanghai Jiao Tong University, Shanghai, China

    Xiaokang Yang

  5. Shanghai University, Shanghai, China

    Ping An

  6. Shanghai Jiao Tong University, Shanghai, China

    Jia Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gong, Y., Lian, X., Ma, X., Xia, Z., Zhou, C. (2023). Combining Transformer and Convolutional Neural Network for Smoke Detection. In: Zhai, G., Zhou, J., Yang, H., Yang, X., An, P., Wang, J. (eds) Digital Multimedia Communications. IFTC 2022. Communications in Computer and Information Science, vol 1766. Springer, Singapore. https://doi.org/10.1007/978-981-99-0856-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-0856-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0855-4

  • Online ISBN: 978-981-99-0856-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation