nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Generative AI for Fire Safety

verfasst von : M. Hamed Mozaffari, Yuchuan Li, Yoon Ko

Erschienen in: Applications of Generative AI

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In the field of fire safety, Generative AI presents promising opportunities to enhance prevention, response, and recovery efforts. In this chapter, we explore the potential applications of Generative AI in fire safety. Generative AI benefits the fire safety field in applications including fires simulation and emergency response training, predictive analytics for fire detection and prediction, evacuation planning and optimization, firefighting robotics, and post-fire reconstruction as well as fire investigation. In this chapter, we provided details of two empirical vision-based examples of employing Generative AI for fire safety applications. These examples show how powerful solutions Generative AI models could be for cases where data shortages have been a hurdle for the advancement of AI for fire safety. With all benefits that Generative AI provides us, careful testing, adherence to safety standards, and collaboration between AI experts and fire safety professionals are crucial to ensure the responsible and effective implementation of Generative AI in the context of fire safety.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Infrared Image Super-Resolution via GAN

Nächstes Kapitel A Multi-scale Convolutional Autoencoder with Attention Mechanism for Fault Diagnosis of Rotating Machinery

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017). Residual attention network for image classification (pp. 3156–3164). https://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Residual_Attention_Network_CVPR_2017_paper.html

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), Article 7553. https://doi.org/10.1038/nature14539

Rajoub, B. (2020). Chapter 3—Supervised and unsupervised learning. In W. Zgallai (Ed.), Biomedical signal processing and artificial intelligence in healthcare (pp. 51–89). Academic Press. https://doi.org/10.1016/B978-0-12-818946-7.00003-2

Mozaffari, M. H. (2020). A real-time and automatic ultrasound-enhanced multimodal second language training system: A deep learning approach [Ph.D. Thesis, Université d’Ottawa/University of Ottawa]. https://doi.org/10.20381/ruor-24710

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Eddy, S. R. (1996). Hidden Markov models. Current Opinion in Structural Biology, 6(3), 361–365. https://doi.org/10.1016/S0959-440X(96)80056-XCrossRef

Jiang, L., Zhang, H., & Cai, Z. (2009). A Novel Bayes model: Hidden Naive Bayes. IEEE Transactions on Knowledge and Data Engineering, 21(10), 1361–1371. https://doi.org/10.1109/TKDE.2008.234CrossRef

Viroli, C., & McLachlan, G. J. (2019). Deep Gaussian mixture models. Statistics and Computing, 29(1), 43–51. https://doi.org/10.1007/s11222-017-9793-zMathSciNetCrossRef

Zhang, N., Ding, S., Zhang, J., & Xue, Y. (2018). An overview on restricted Boltzmann machines. Neurocomputing, 275, 1186–1199. https://doi.org/10.1016/j.neucom.2017.09.065CrossRef

10.

Hinton, G. (2009). Deep belief networks. Scholarpedia, 4(5), 5947. https://doi.org/10.4249/scholarpedia.5947CrossRef

11.

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53–65. https://doi.org/10.1109/MSP.2017.2765202CrossRef

12.

Zhai, J., Zhang, S., Chen, J., & He, Q. (2018). Autoencoder and its various variants. In 2018 IEEE international conference on systems, man, and cybernetics (SMC), (pp. 415–419). https://doi.org/10.1109/SMC.2018.00080

13.

Wong, C. S., & Li, W. K. (2000). On a mixture autoregressive model. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(1), 95–115. https://doi.org/10.1111/1467-9868.00222MathSciNetCrossRef

14.

Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. https://doi.org/10.1109/78.650093CrossRef

15.

Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. ArXiv Preprint arXiv:1312.6114

16.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Lukasz, & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

17.

Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2023). A survey of generative AI applications (arXiv:2306.02781). arXiv. https://doi.org/10.48550/arXiv.2306.02781

18.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139–144.MathSciNetCrossRef

19.

Gillioz, A., Casas, J., Mugellini, E., & Abou Khaled, O. (2020). Overview of the transformer-based models for NLP Tasks. In 2020 15th Conference on computer science and information systems (FedCSIS) (pp. 179–183).

20.

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, (pp. 2223–2232).

21.

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., & Funtowicz, M. (2020). Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 conference on empirical methods in natural language processing: System demonstrations, (pp. 38–45).

22.

Shaw, P., Uszkoreit, J., & Vaswani, A. (2018). Self-attention with relative position representations. ArXiv Preprint arXiv:1803.02155

23.

Oussidi, A., & Elhassouny, A. (2018). Deep generative models: Survey. International Conference on Intelligent Systems and Computer Vision (ISCV), 2018, 1–8.

24.

Zhou, L., Cai, C., Gao, Y., Su, S., & Wu, J. (2018). Variational autoencoder for low bit-rate image compression. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, (pp. 2617–2620).

25.

Spinner, T., Körner, J., Görtler, J., & Deussen, O. (2018). Towards an interpretable latent space: An intuitive comparison of autoencoders with variational autoencoders. IEEE VIS 2018.

26.

Ko, Y., Mozaffari, M. H., & Li, Y. (2023). Fire and smoke image recognition. In Intelligent building fire safety and smart firefighting. Springer.

27.

Dung, N. Q., & Kim, H. (2022). Generating high-resolution fire images with controllable attributes via generative adversarial networks. In 2022 22nd International conference on control, automation and systems (ICCAS) (pp. 348–353). https://doi.org/10.23919/ICCAS55662.2022.10003687

28.

Qin, K., Hou, X., Yan, Z., Zhou, F., & Bu, L. (2022). FGL-GAN: Global-local mask generative adversarial network for flame image composition. Sensors, 22(17), Article 17. https://doi.org/10.3390/s22176332

29.

Yun, K., Bustos, J., & Lu, T. (2018). Predicting rapid fire growth (Flashover) using conditional generative adversarial networks (arXiv:1801.09804). arXiv. https://doi.org/10.48550/arXiv.1801.09804

30.

Kacker, T., Perrusquia, A., & Guo, W. (2023). Multi-spectral fusion using generative adversarial networks for UAV detection of wild fires. In 2023 International conference on artificial intelligence in information and communication (ICAIIC) (pp. 182–187). https://doi.org/10.1109/ICAIIC57133.2023.10067042

31.

Park, M., Tran, D. Q., Bak, J., & Park, S. (2022). Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization. International Journal of Applied Earth Observation and Geoinformation, 114, 103052. https://doi.org/10.1016/j.jag.2022.103052CrossRef

32.

Cheng, S., Ma, J., & Zhang, S. (2019). Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network. Journal of Electronic Imaging, 28(3), 033006. https://doi.org/10.1117/1.JEI.28.3.033006CrossRef

33.

Geetha, S., Abhishek, C. S., & Akshayanat, C. S. (2021). Machine vision based fire detection techniques: A survey. Fire Technology, 57(2), 591–623. https://doi.org/10.1007/s10694-020-01064-zCrossRef

34.

Chaturvedi, S., Khanna, P., & Ojha, A. (2022). A survey on vision-based outdoor smoke detection techniques for environmental safety. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 158–187. https://doi.org/10.1016/j.isprsjprs.2022.01.013CrossRef

35.

Fahrmeir, L., Kneib, T., Lang, S., & Marx, B. D. (2022). Regression models. In Regression: Models, methods and applications (pp. 23–84). Springer.

36.

Mozaffari, M., Li, Y., & Ko, Y. (2022). Detecting flashover in a room fire based on the sequence of thermal infrared images using convolutional neural networks. Proceedings of the Canadian conference on artificial intelligence.

37.

Kuang, X., Zhu, J., Sui, X., Liu, Y., Liu, C., Chen, Q., & Gu, G. (2020). Thermal infrared colorization via conditional generative adversarial network. Infrared Physics and Technology, 107, 103338. https://doi.org/10.1016/j.infrared.2020.103338CrossRef

38.

Li, Y., Ko, Y., & Lee, W. (2022). RGB image-based hybrid model for automatic prediction of flashover in compartment fires. Fire Safety Journal, 132, 103629. https://doi.org/10.1016/j.firesaf.2022.103629CrossRef

39.

Yi, S., Li, J., & Yuan, X. (2021). DFPGAN: Dual fusion path generative adversarial network for infrared and visible image fusion. Infrared Physics and Technology, 119, 103947. https://doi.org/10.1016/j.infrared.2021.103947CrossRef

40.

Wang, Z., Chen, J., & Hoi, S. C. H. (2021). Deep learning for image super-resolution: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3365–3387. https://doi.org/10.1109/TPAMI.2020.2982166CrossRef

41.

Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., & Song, M. (2020). Neural style transfer: A review. IEEE Transactions on Visualization and Computer Graphics, 26(11), 3365–3385. https://doi.org/10.1109/TVCG.2019.2921336CrossRef

42.

Chu, C., Zhmoginov, A., & Sandler, M. (2017). Cyclegan, a master of steganography. ArXiv Preprint arXiv:1712.02950

43.

Li, Y. (2021). Dual-attention generative adversarial network and flame and smoke analysis [Master’s Thesis]. Université d’Ottawa/University of Ottawa.

44.

Bawalya, A., Gibbs, E., Lougheed, G., & Kashef, A. (2023, June 7). Characterization of fires in multi-suite residential dwellings: Final project report: Part 1-A compilation of post-flashover room fire test data—NRC Publications Archive. https://nrc-publications.canada.ca/eng/view/object/?id=a46dbb8a-5093-4d5e-b5d3-07bbc866fd9a

45.

Li, Y., Lee, W., & Ko, Y. (2023). A feasibility study on translation of RGB images to thermal images: Development of a machine learning algorithm. Springer Nature SN Computer Science. Under Publication.

46.

Sun, Y., Wang, Y., Hu, L., Huang, Y., Liu, H., Wang, S., & Zhang, C. (2023). Attribute-Guided generative adversarial network with improved episode training strategy for few-shot SAR image generation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1785–1801.CrossRef

47.

Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 28.

48.

Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., & Khudanpur, S. (2010). Recurrent neural network based language model. Interspeech, 2(3), 1045–1048.CrossRef

49.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.CrossRef

50.

Mozaffari, M. H., Li, Y., & Ko, Y. (2023). Real-time detection and forecast of flashovers by the visual room fire features using deep convolutional neural networks. Journal of Building Engineering, 64, 105674.CrossRef

51.

Ndajah, P., Kikuchi, H., Yukawa, M., Watanabe, H., & Muramatsu, S. (2010). SSIM image quality metric for denoised images. In International conference on visualization, imaging and simulation—Proceedings (p. 57).

Titel: Generative AI for Fire Safety
verfasst von: M. Hamed Mozaffari
Yuchuan Li
Yoon Ko
Verlag: Springer International Publishing
Buch: Applications of Generative AI
Print ISBN: 978-3-031-46237-5

Electronic ISBN: 978-3-031-46238-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-46238-2_29

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner