Skip to main content
Springer Nature Link
Log in

ECS-STPM: An Efficient Model for Tunnel Fire Anomaly Detection

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14334))

  • 328 Accesses

Abstract

The fire spreads rapidly in the tunnel due to the narrow space and high sealing, which makes rescue hard and threatens the citizen’s lives. However, the lack of public fire datasets makes it challenging for networks to learn targeted representations of fire features, resulting in low detection accuracy. To tackle this problem, we construct a Tunnel Fire Anomaly Detection (TF-AD) dataset based on unsupervised training. This dataset contains 5200 high-resolution color images, including non-fire images for training and fire images with annotations for testing. Based on the TF-AD dataset, we propose an efficient tunnel fire anomaly detection model named ECS-STPM. ECS-STPM consists of a teacher and student network with identical EfficientNet-B1 structures. Additionally, considering the efficiency of adaptively assigning channel weights, we combine the convolutional kernel with channels to propose a novel attention mechanism, Efficient Kernel and Channel Attention (EKCA). EKCA replaces the Squeeze-and-Excitation (SE) networks in the MBConv module to prevent the loss of crucial information. Furthermore, we introduce the SPD-Conv module instead of the strided convolution layer to increase the detection accuracy in smaller fire areas. The experimental results on TF-AD dataset show that the pixel-level AUC-ROC and image-level AUC-ROC are up to 0.931 and 0.835, which verifies the effectiveness of our model.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39

    Chapter  Google Scholar 

  2. Association NFP: Highway vehicle fires (2014-2016). NFPA Fire Analysis and Research Division (2018). https://www.nfpa.org/-/media/Files/News-and-Research/Fire-statistics-and-reports/Vehicle%20fires/osvehiclefires.pdf

  3. Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4183–4192 (2020)

    Google Scholar 

  4. Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)

  5. Company D: 895 fire videos data. https://www.datatang.ai/datasets/92?utm_source=PaperwithCode &utm_medium=dataset

  6. Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 475–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_35

    Chapter  Google Scholar 

  7. Dunnings, A.J., Breckon, T.P.: Experimentally defined convolutional neural network architecture variants for non-temporal real-time fire detection. In: 2018 25th IEEE international conference on image processing (ICIP), pp. 1558–1562. IEEE (2018)

    Google Scholar 

  8. Fang, U., Li, M., Li, J., Gao, L., Jia, T., Zhang, Y.: A comprehensive survey on multi-view clustering. IEEE Trans. Knowl. Data Eng. (2023)

    Google Scholar 

  9. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  10. Huang, C., Ye, F., Cao, J., Li, M., Zhang, Y., Lu, C.: Attribute restoration framework for anomaly detection. arXiv preprint arXiv:1911.10676 (2019)

  11. Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 510–519 (2019)

    Google Scholar 

  12. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  13. Napoletano, P., Piccoli, F., Schettini, R.: Anomaly detection in nanofibrous materials by cnn-based self-similarity. Sensors 18(1), 209 (2018)

    Article  Google Scholar 

  14. Reiss, T., Cohen, N., Horwitz, E., Abutbul, R., Hoshen, Y.: Anomaly detection requires better representations. In: Computer Vision–ECCV 2022 Workshops: Proceedings, Part IV, pp. 56–68. Springer (2023). https://doi.org/10.1007/978-3-031-25069-9_4

  15. Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12

    Chapter  Google Scholar 

  16. Sharma, J., Granmo, O.C., Goodwin, M., Fidje, J.T.: Deep convolutional neural networks for fire detection in images. In: Engineering Applications of Neural Networks: 18th International Conference, EANN 2017, pp. 183–193. Springer (2017). https://doi.org/10.1007/978-3-319-65172-9_16

  17. Song, X., Li, J., Cai, T., Yang, S., Yang, T., Liu, C.: A survey on deep learning based knowledge tracing. Knowl.-Based Syst. 258, 110036 (2022)

    Article  Google Scholar 

  18. Gustavo Botelho de Sousa, R.d.S.T.: Bowfire. https://bitbucket.org/gbdi/bowfire-dataset/downloads/

  19. Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843–852 (2017)

    Google Scholar 

  20. Sunkara, R., Luo, T.: No more strided convolutions or pooling: A new cnn building block for low-resolution images and small objects. arXiv preprint arXiv:2208.03641 (2022)

  21. Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  22. University, D.: Fire-dunnings-dataset. https://collections.durham.ac.uk/files/r2d217qp536#.YG_uqsgzqkL

  23. Wang, G., Han, S., Ding, E., Huang, D.: Student-teacher feature pyramid matching for unsupervised anomaly detection. arXiv preprint arXiv:2103.04257 (2021)

  24. Wang, L., Yoon, K.J.: Knowledge distillation and student-teacher learning for visual intelligence: a review and new outlooks. IEEE Trans. Pattern Analy. Mach. Intell. (2021)

    Google Scholar 

  25. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534–11542 (2020)

    Google Scholar 

  26. Wang, Y., Dang, L., Ren, J.: Forest fire image recognition based on convolutional neural network. J. Algorithms Comput. Technol. 13, 1748302619887689 (2019)

    Article  Google Scholar 

  27. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1

    Chapter  Google Scholar 

  28. Wu, S., Zhang, L.: Using popular object detection methods for real time forest fire detection. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 280–284. IEEE (2018)

    Google Scholar 

  29. Xu, C., Zhao, W., Zhao, J., Guan, Z., Song, X., Li, J.: Uncertainty-aware multiview deep learning for internet of things applications. IEEE Trans. Industr. Inf. 19(2), 1456–1466 (2022)

    Article  Google Scholar 

  30. Zhang, P.F., Luo, Y., Huang, Z., Xu, X.S., Song, J.: High-order nonlocal hashing for unsupervised cross-modal retrieval. World Wide Web 24, 563–583 (2021)

    Article  Google Scholar 

  31. Zhang, Q.x., Lin, G.h., Zhang, Y.m., Xu, G., Wang, J.j.: Wildland forest fire smoke detection based on faster r-cnn using synthetic smoke images. Proc. Eng. 211, 441–446 (2018)

    Google Scholar 

  32. Zhou, X., Delicato, F.C., Wang, K.I.K., Huang, R.: Smart computing and cyber technology for cyberization. World Wide Web 23, 1089–1100 (2020)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported in part by the National Natural Fund Joint Fund Project of China under Grant U21B2041.

Author information

Authors and Affiliations

  1. Chang’an University, Xi’an, China

    Huansheng Song, Ya Wen & ShiJie Sun

  2. Swinburne University of Technology, Melbourne, Australia

    Xiangyu Song

  3. Macquarie University, Sydney, Australia

    Taotao Cai

  4. Deakin University, Victoria, Australia

    Jianxin Li

Authors
  1. Huansheng Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ya Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangyu Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ShiJie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Taotao Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jianxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangyu Song .

Editor information

Editors and Affiliations

  1. Peng Cheng Laboratory, Shenzhen, China

    Xiangyu Song

  2. China University of Geosciences, Wuhan, China

    Ruyi Feng

  3. China University of Geosciences, Wuhan, China

    Yunliang Chen

  4. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  5. University of Exeter, Exeter, UK

    Geyong Min

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Song, H., Wen, Y., Song, X., Sun, S., Cai, T., Li, J. (2024). ECS-STPM: An Efficient Model for Tunnel Fire Anomaly Detection. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-2421-5_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2420-8

  • Online ISBN: 978-981-97-2421-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics