nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Vision Transformer-Based Forest Fire Classification: Wild Life Management System

verfasst von : Anandakumar Haldorai, Babitha Lincy R, Suriya Murugan, Minu Balakrishnan

Erschienen in: Artificial Intelligence for Sustainable Development

Verlag: Springer Nature Switzerland

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

One of the natural resources in the world is forest, because the ecosystems are directly influenced by the forest area. So, to preserve the strong and blossoming ecosystem, forest maintenance is undoubtably necessary. In recent time, especially at summer season, forest fire is one of the serious problems. This forest may collapse the eco system. So early detection of fire in forest area is important. The computer vision with artificial intelligence is one of the successful research areas, which is still providing exact solutions for many problems. Accordingly, this chapter proposed Vision Transformer Model (ViTM) for early forest fire detection. The performance of the proposed system is analyzed with various performance metrics successfully. The planned early fire detection model produced 98.7% accuracy on the classification of fire and no-fire dataset. From the result, we can conclude that the proposed ViTM is an exclusive fire detection model.

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 Automatic Human Activity Detection Using Novel Deep Learning Architecture

Nächstes Kapitel A Modified AI Model for Automatic and Precision Monitoring System of Wildlife in Forest Areas

X. Chen et al., “Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset,” in IEEE Access, vol. 10, pp. 121301–121317, 2022, https://doi.org/10.1109/ACCESS.2022.3222805.

D. Q. Tran, M. Park, Y. Jeon, J. Bak and S. Park, “Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data,” in IEEE Access, vol. 10, pp. 66061–66071, 2022, https://doi.org/10.1109/ACCESS.2022.3184707.

H. Yar, T. Hussain, M. Agarwal, Z. A. Khan, S. K. Gupta and S. W. Baik, “Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark,” in IEEE Transactions on Image Processing, vol. 31, pp. 6331–6343, 2022, https://doi.org/10.1109/TIP.2022.3207006.

J. S. Almeida, C. Huang, F. G. Nogueira, S. Bhatia and V. H. C. de Albuquerque, “EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire–Smoke Detection,” in IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7889–7898, Nov. 2022, https://doi.org/10.1109/TII.2021.3138752.

F. H. Panahi, F. H. Panahi and T. Ohtsuki, “A Reinforcement Learning-Based Fire Warning and Suppression System Using Unmanned Aerial Vehicles,” in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–16, 2023, Art no. 5500216, https://doi.org/10.1109/TIM.2022.3227558.

W. Bo, J. Liu, X. Fan, T. Tjahjadi, Q. Ye and L. Fu, “BASNet: Burned Area Segmentation Network for Real-Time Detection of Damage Maps in Remote Sensing Images,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022, Art no. 5627913, https://doi.org/10.1109/TGRS.2022.3197647.

L. Li et al., “Estimation of Ground Water Level (GWL) for Tropical Peatland Forest Using Machine Learning,” in IEEE Access, vol. 10, pp. 126180–126187, 2022, https://doi.org/10.1109/ACCESS.2022.3225906.

M. Amanullah, S. Thanga Ramya, M. Sudha, V. P. Gladis Pushparathi, A. Haldorai, and B. Pant, “Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction,” Journal of Intelligent Fuzzy Systems, vol. 44, no. 3, pp. 3867–3876, Mar. 2023, https://doi.org/10.3233/jifs-220480.

A. Haldorai and K. K, “An Analysis of Software Defined Networks and Possibilities of Network Attacks,” Journal of Machine and Computing, pp. 42–52, Jan. 2022, https://doi.org/10.53759/7669/jmc202202006.

10.

J. Shi, W. Wang, Y. Gao and N. Yu, “Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras,” in IEEE Access, vol. 8, pp. 72326–72339, 2020, https://doi.org/10.1109/ACCESS.2020.2987991.

11.

K. M. Keerthi, “Optical Flow Estimation for Efficient Flame Detection in Videos,” Proceedings of the International Conference on Emerging Trends in Engineering & Technology (ICETET-2015), 2015, https://doi.org/10.3850/978-981-09-5346-1_cse-535.

12.

V. E. Sathishkumar, J. Cho, M. Subramanian, and O. S. Naren, “Forest fire and smoke detection using deep learning-based learning without forgetting,” Fire Ecology, vol. 19, no. 1, Feb. 2023, https://doi.org/10.1186/s42408-022-00165-0.

13.

B. Kizilkaya, E. Ever, H. Y. Yatbaz, and A. Yazici, “An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 2, pp. 1–21, Feb. 2022, https://doi.org/10.1145/3473037.CrossRef

14.

M. Park and B. C. Ko, “Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube,” Sensors, vol. 20, no. 8, p. 2202, Apr. 2020, https://doi.org/10.3390/s20082202.CrossRef

15.

Y. Hu et al., “Fast forest fire smoke detection using MVMNet,” Knowledge-Based Systems, vol. 241, p. 108219, Apr. 2022, https://doi.org/10.1016/j.knosys.2022.108219.CrossRef

16.

S. Yang, S. Zhang, X. Chen, J. Li, E. Li, and W. Chen, “A Fire Detection Method based on Computer Vision,” 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), May 2022, https://doi.org/10.1109/icetci55101.2022.9832265.

17.

S. T. Seydi, V. Saeidi, B. Kalantar, N. Ueda, and A. A. Halin, “Fire-Net: A Deep Learning Framework for Active Forest Fire Detection,” Journal of Sensors, vol. 2022, pp. 1–14, Feb. 2022, https://doi.org/10.1155/2022/8044390.CrossRef

18.

L. Zhao, L. Zhi, C. Zhao, and W. Zheng, “Fire-YOLO: A Small Target Object Detection Method for Fire Inspection,” Sustainability, vol. 14, no. 9, p. 4930, Apr. 2022, https://doi.org/10.3390/su14094930.CrossRef

19.

F. Abid, “A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems,” Fire Technology, vol. 57, no. 2, pp. 559–590, Nov. 2020, https://doi.org/10.1007/s10694-020-01056-z.CrossRef

20.

B. C. Ko, K.-H. Cheong, and J.-Y. Nam, “Fire detection based on vision sensor and support vector machines,” Fire Safety Journal, vol. 44, no. 3, pp. 322–329, Apr. 2009, https://doi.org/10.1016/j.firesaf.2008.07.006.CrossRef

21.

S. G. Kong, D. Jin, S. Li, and H. Kim, “Fast fire flame detection in surveillance video using logistic regression and temporal smoothing,” Fire Safety Journal, vol. 79, pp. 37–43, Jan. 2016, https://doi.org/10.1016/j.firesaf.2015.11.015.

22.

J. Kim, J. K. Lee, and K. M. Lee, “Accurate Image Super-Resolution Using Very Deep Convolutional Networks,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, https://doi.org/10.1109/cvpr.2016.182.

23.

C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, https://doi.org/10.1109/cvpr.2015.7298594.

24.

P. Li and W. Zhao, “Image fire detection algorithms based on convolutional neural networks,” Case Studies in Thermal Engineering, vol. 19, p. 100625, Jun. 2020, https://doi.org/10.1016/j.csite.2020.100625.

25.

H. A. Hosni Mahmoud, A. H. Alharbi, and N. S. Alghamdi, “Time-Efficient Fire Detection Convolutional Neural Network Coupled with Transfer Learning,” Intelligent Automation & Soft Computing, vol. 31, no. 3, pp. 1393–1403, 2022, https://doi.org/10.32604/iasc.2022.020629.CrossRef

26.

A. Bari, T. Saini, and A. Kumar, “Fire Detection Using Deep Transfer Learning on Surveillance Videos,” 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Feb. 2021, https://doi.org/10.1109/icicv50876.2021.9388485.

27.

X. Cheng, “Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition,” Advances in Multimedia, vol. 2021, pp. 1–7, Nov. 2021, https://doi.org/10.1155/2021/6147860.

28.

D. Alexandrov, E. Pertseva, I. Berman, I. Pantiukhin, and A. Kapitonov, “Analysis of Machine Learning Methods for Wildfire Security Monitoring with an Unmanned Aerial Vehicles,” 2019 24th Conference of Open Innovations Association (FRUCT), Apr. 2019, https://doi.org/10.23919/fruct.2019.8711917.

29.

J. Zhang, L. Ye, and Y. Lai, “Stock Price Prediction Using CNN-BiLSTM-Attention Model,” Mathematics, vol. 11, no. 9, p. 1985, Apr. 2023, https://doi.org/10.3390/math11091985.

30.

Jiao, Z.; Zhang, Y.; Mu, L.; Xin, J.; Jiao, S.; Liu, H.; Liu, D. A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire Detection. In Proceedings of the Chinese Control and Decision Conference (CCDC), Hefei, China, 22–24 August 2020; IEEE: New York, NY, USA, 2020; pp. 4963–4967.

31.

Li, W.; Yu, Z. A Lightweight Convolutional Neural Network Flame Detection Algorithm. In Proceedings of the IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 18–20 June 2021; IEEE: New York, NY, USA, 2021; pp. 83–86.

32.

S. Lee and Y. J. Jung, “Hint-Based Image Colorization Based on Hierarchical Vision Transformer,” Sensors, vol. 22, no. 19, p. 7419, Sep. 2022, https://doi.org/10.3390/s22197419.

33.

Kaabi, R.; Sayadi, M.; Bouchouicha, M.; Fnaiech, F.; Moreau, E.; Ginoux, J.M. Early smoke detection of forest wildfire video using deep belief network. In Proceedings of the International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, 21–24 March 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6.

34.

Y. Zhao, J. Ma, X. Li, and J. Zhang, “Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery,” Sensors, vol. 18, no. 3, p. 712, Feb. 2018, https://doi.org/10.3390/s18030712.CrossRef

35.

Chen, Y.; Zhang, Y.; Xin, J.; Wang, G.; Mu, L.; Yi, Y.; Liu, H.; Liu, D. UAV Image-based Forest Fire Detection Approach Using Convolutional Neural Network. In Proceedings of the IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 19–21 June 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 2118–2123.

36.

Cao, Y.; Yang, F.; Tang, Q.; Lu, X. An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition. IEEE Access 2019, 7, 154732–154742.CrossRef

37.

Sousa, M.J.; Moutinho, A.; Almeida, M. Wildfire detection using transfer learning on augmented datasets. Expert Syst. Appl. 2019, 142, 112975.CrossRef

38.

Tang, Y.; Feng, H.; Chen, J.; Chen, Y. ForestResNet: A Deep Learning Algorithm for Forest Image Classification. J. Physics Conf.Ser. 2021, 2024, 012053.CrossRef

39.

S. S. C, B. L. R and D. S, “Design and Analysis of CNN based Residue Number System for Performance Enhancement,” 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 1182–1187, https://doi.org/10.1109/ICAIS56108.2023.10073805.

40.

Govil, K.;Welch, M.L.; Ball, J.T.; Pennypacker, C.R. Preliminary Results from aWildfire Detection System Using Deep Learning on Remote Camera Images. Remote Sens. 2020, 12, 166.CrossRef

41.

Park, M.; Tran, D.Q.; Lee, S.; Park, S. Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response. Remote Sens. 2021, 13, 3985.CrossRef

42.

Qin, YY., Cao, JT. & Ji, XF. Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3. Int. J. Autom. Comput. 18, 300–310 (2021). https://doi.org/10.1007/s11633-020-1269-5

43.

Jeon, M., H.-S. Choi, J. Lee, and M. Kang. 2021. Multi-scale prediction for fire detection using convolutional neural network. Fire Technology 57 (5): 2533–2551.CrossRef

44.

P. V. A. B. de Venâncio, A. C. Lisboa, and A. V. Barbosa, “An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices,” Neural Computing and Applications, vol. 34, no. 18, pp. 15349–15368, Jun. 2022, https://doi.org/10.1007/s00521-022-07467-z.CrossRef

45.

Dampage, U., L. Bandaranayake, R. Wanasinghe, K. Kottahachchi, and B. Jayasanka. 2022. Forest fire detection system using wireless sensor networks and machine learning. Scientific reports 12 (1): 1–11.CrossRef

46.

R. Dogan and M. E. Karsligil, “Fire detection using color and motion features in video sequences,” 2010 IEEE 18th Signal Processing and Communications Applications Conference, Apr. 2010, https://doi.org/10.1109/siu.2010.5651727.

47.

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby , “An Image is Worth 16*16 Words: Transformers for Image Recognition at Scale” ICLR 2021.

48.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin , “Attention Is All You Need”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.

Titel: Vision Transformer-Based Forest Fire Classification: Wild Life Management System
verfasst von: Anandakumar Haldorai
Babitha Lincy R
Suriya Murugan
Minu Balakrishnan
Verlag: Springer Nature Switzerland
Buch: Artificial Intelligence for Sustainable Development
Print ISBN: 978-3-031-53971-8

Electronic ISBN: 978-3-031-53972-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53972-5_24

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Die Gewinner und Laudatoren des Sustainability Award in Automotive 2024/© Uli Regenscheit | ATZlive, Search Icon, Banner Hanser, Additiv gefertigte Teile/© Marina_Skoropadskaya | Getty Images | iStock, Warnschild "Land unter"/© Bluedesign / Fotolia, Gardiner von Trapp/© Alpega Group, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade, chassis.tech plus 2023/© [M] ATZlive / TÜV SÜD PRODUCT SERVICE GMBH, adäsion-Webinar-Matinee/© krystiannawrocki_ Getty Images

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"

Neuer Inhalt

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

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