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Advancing fire detection: two-stage deep learning with hybrid feature extraction using faster R-CNN approach

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Abstract

Fire incidents pose severe threats to life, property, and the environment, accounting for significant losses worldwide. Traditional sensing technologies exhibit limitations in effectively detecting fires, particularly in larger spaces. The application of deep learning techniques on fire detection systems has been widely explored. However, many challenges are associated with fire detection technologies, especially in scenarios like indoor fires and forest fires, as well as whether the fire is accompanied by smoke or not. Which results in substantial environmental losses and long-term recovery periods. Early sensing technologies lacked effectiveness in detecting fire instances in open spaces due to response delays and failure to utilize static and dynamic features. In this paper, we aimed to address these challenges by proposing a two-stage fire detection approach using deep learning techniques. The approach proposed a new Faster R-CNN architecture, including our proposed hybrid feature extractor. Evaluation yields a mAP@0.5 of 90.1% with an accuracy of 96.5%. The outcomes indicate that our new hybrid feature extractor surpasses the effectiveness of conventional single backbone transfer learning methods and Yolo’s one-stage detection approach in accurately identifying flames and smoke in various indoor and outdoor settings, providing an accurate fire detection system.

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Availability of data

The dataset is available in [22].

References

  1. Gaur, A., Singh, A.K., Kumar, A., Kumar, A., Kapoor, K.: Video flame and smoke based fire detection algorithms: a literature review. Fire Technol. 1, 1–38 (2020)

    Google Scholar 

  2. Fire, I.A., Services, R.: World fire statistics. Report No 27, 1–65 (2022)

  3. Seydi, S.T., Saeidi, V., Kalantar, B., Ueda, N., Halin, A.A.: Fire-net: a deep learning framework for active forest fire detection. J. Sens. 2022, 1–14 (2022)

    Google Scholar 

  4. Gaur, A., Singh, A.K., Kumar, A., Kulkarni, K.S., Lala, S., Kapoor, K., Srivastava, V., Kumar, A., Mukhopadhyay, S.C.: Fire sensing technologies: a review. IEEE Sens. J. 19, 3191–3202 (2019)

    Article  Google Scholar 

  5. Boudouh, S.S., Bouakkaz, M.: New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques. Multimed. Tools Appl. (2023)

  6. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 1, 1–62 (2020)

    Google Scholar 

  7. Boudouh, S.S., Bouakkaz, M.: Enhanced breast mass mammography classification approach based on pre-processing and hybridization of transfer learning models. J. Cancer Res. Clin. Oncol. 149, 14549–14564 (2023)

    Article  Google Scholar 

  8. Pan, S.J., Tsang, I.W.-H., Kwok, J.T.-Y., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks 22, 199–210 (2009)

    Article  Google Scholar 

  9. Zhou, J., Yang, X., Zhang, L., Shao, S., Bian, G.: Multisignal vgg19 network with transposed convolution for rotating machinery fault diagnosis based on deep transfer learning. Shock. Vib. 2020, 1–12 (2020)

    Article  Google Scholar 

  10. Shivang Agarwal, F.J. Jean Ogier du Terrail: Recent advances in object detection in the age of deep convolutional neural networks (2019)

  11. Enoch Arulprakash, M.A.: A study on generic object detection with emphasis on future research directions. J. King Saud Univ. Comput. Inf. Sci. 34(9), 1 (2022)

  12. Pan, H., Badawi, D., Zhang, X., Cetin, A.E.: Additive neural network for forest fire detection. SIViP 14, 675–682 (2019)

    Article  Google Scholar 

  13. Wang, Y., Hua, C., Ding, W., Wu, R.: Real-time detection of flame and smoke using an improved yolov4 network. SIViP 16, 1109–1116 (2022)

  14. Khan, S.H., Muhammad, K., Hussain, T., Ser, J.D., Cuzzolin, F., Bhattacharyya, S., Akhtar, Z., Albuquerque, V.H.: Deepsmoke: Deep learning model for smoke detection and segmentation in outdoor environments. Expert. Syst. Appl. (2021)

  15. Dewangan, A., Pande, Y., Braun, H.-W., Vernon, F.L., Pérez, I., Altintas, I., Cottrell, G., Nguyen, M.H.: Figlib & smokeynet: Dataset and deep learning model for real-time wildland fire smoke detection. ArXiv (2021)

  16. Pan, H., Badawi, D., Zhang, X., Cetin, A.E.: Additive neural network for forest fire detection. SIViP 14, 675–682 (2019)

    Article  Google Scholar 

  17. Dai, P., Zhang, Q., Lin, G., Shafique, M., Huo, Y., Tu, R., Zhang, Y.: Multi-scale video flame detection for early fire warning based on deep learning. In: Frontiers in Energy Research (2022). https://api.semanticscholar.org/CorpusID:247254603

  18. Avazov, K., Mukhiddinov, M., Makhmudov, F., Cho, Y.I.: Fire detection method in smart city environments using a deep-learning-based approach. Electronics (2021)

  19. Yandouzi, M., Grari, M., Berrahal, M., Idrissi, I., Moussaoui, O., Azizi, M., Ghoumid, K., Elmiad, A.K.: Investigation of combining deep learning object recognition with drones for forest fire detection and monitoring. Int. J. Adv. Comput. Sci. Appl. (2023)

  20. Dou, Z.-l., Zhou, H., Liu, Z., Hu, Y., Wang, P., Zhang, J., Wang, Q., Chen, L.-J., Diao, X., Li, J.: An improved yolov5s fire detection model. Fire Technol. (2023)

  21. Mohammed, R.K.: A real-time forest fire and smoke detection system using deep learning (2022)

  22. Cheknane, M.: Best fire and smoke Dataset. Roboflow. visited on 2024-04-14 (2023). https://universe.roboflow.com/mar-27kzg/best-fire-and-smoke

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Funding

This study was not funded. The authors have no relevant financial or non-financial interests to disclose.

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  1. LIM Laboratory, University of Laghouat, Laghouat, Algeria

    Maroua Cheknane, Tahar Bendouma & Saida Sarra Boudouh

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  1. Maroua Cheknane
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  2. Tahar Bendouma
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  3. Saida Sarra Boudouh
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Correspondence to Saida Sarra Boudouh.

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Cheknane, M., Bendouma, T. & Boudouh, S.S. Advancing fire detection: two-stage deep learning with hybrid feature extraction using faster R-CNN approach. SIViP (2024). https://doi.org/10.1007/s11760-024-03250-w

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  • DOI: https://doi.org/10.1007/s11760-024-03250-w

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