International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Application of Deep Learning: A Review for Firefighting

  • 투고 : 2022.05.05
  • 발행 : 2022.05.30
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초록

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

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참고문헌

  1. Bhattarai, M., Jensen-Curtis, A. R., &Martinez-Ramon, M. (2020). An embedded deep learning system for augmented reality in firefighting applications. arXiv preprint arXiv:2009.10679.
  2. Bhattarai, M., & Martinez-Ramon, M. (2020). A deep learning framework for detection of targets in thermal images to improve firefighting. IEEE Access, 8, 88308-88321. https://doi.org/10.1109/access.2020.2993767
  3. Oskooei, A., Chau, S. M., Weiss, J., Sridhar, A., Martinez, M. R., & Michel, B. (2021). Destress: deep learning for unsupervised identification of mental stress in firefighters from heart-rate variability (hrv) data. In Explainable AI in Healthcare and Medicine (pp. 93-105). Springer, Cham.
  4. Yang, Z., & Liu, Y. (2018, July). Investigating the influential factors on firefighter injuries using statistical machine learning. In 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 422-427). IEEE.
  5. Chau, S. M. (2020). Firefighter Virtual Reality Simulation for Personalized Stress Detection. In KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI, Bamberg, Germany, September 21-25, 2020, Proceedings (Vol. 12325, p. 343). Springer Nature.
  6. Bhattarai, M. (2018). End-to-End Deep Learning Systems for Scene Understanding, Path Planning and Navigation in Fire Fighter Teams.
  7. Yun, K., Lu, T., & Chow, E. (2018, April). Occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks. In Pattern Recognition and Tracking XXIX (Vol. 10649, p. 106490T). International Society for Optics and Photonics.
  8. Hackett, S., Cai, Y., & Siegel, M. (2019, October). Activity Recognition from Sensor Fusion on Fireman's Helmet. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-6). IEEE.
  9. Bhattarai, M., & Martinez-Ramon, M. (2020). A deep Q-Learning based Path Planning and Navigation System for Firefighting Environments. arXiv preprint arXiv:2011.06450.
  10. Wang, Y., Shi, Y., Du, J., Lin, Y., & Wang, Q. (2020). A CNN-based personalized system for attention detection in wayfinding tasks. Advanced Engineering Informatics, 46, 101180. https://doi.org/10.1016/j.aei.2020.101180
  11. Saputra, M. R. U., de Gusmao, P. P., Lu, C. X., Almalioglu, Y., Rosa, S., Chen, C., ... &Trigoni, N. (2020). Deeptio: A deep thermal-inertial odometry with visual hallucination. IEEE Robotics and Automation Letters, 5(2), 1672-1679. https://doi.org/10.1109/lra.2020.2969170
  12. Wagstaff, B., & Kelly, J. (2018, September). LSTM-based zero-velocity detection for robust inertial navigation. In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1-8). IEEE.
  13. Yang, L., Liang, Y., Wu, D., & Gault, J. (2018, October). Train and equip firefighters with cognitive virtual and augmented reality. In 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC) (pp. 453-459). IEEE.
  14. Roy, S., & Rahman, M. S. (2019, February). Emergency Vehicle Detection on Heavy Traffic Road from CCTV Footage Using Deep Convolutional Neural Network. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.
  15. Xu, D., Song, Y., Meng, Y., Istvan, B., &Gu, Y. (2020). Relationship between Firefighter Physical Fitness and Special Ability Performance: Predictive Research Based on Machine Learning Algorithms. International Journal of Environmental Research and Public Health, 17(20), 7689. https://doi.org/10.3390/ijerph17207689
  16. Zohdi, T. I. (2021). A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety. Computer Methods in Applied Mechanics and Engineering, 373, 113446. https://doi.org/10.1016/j.cma.2020.113446
  17. Teixeira, L. P. P. B. (2020). Predictive analytics applied to firefighter response, a practical approach (Doctoral dissertation).
  18. Sandulescu, V., &Dobrescu, R. (2015, November). Wearable system for stress monitoring of firefighters in special missions. In 2015 E-Health and Bioengineering Conference (EHB) (pp. 1-4). IEEE.
  19. Kupschick, S., Pendzich, M., Gardas, D., Jurgensohn, T., Wischniewski, S., & Adolph, L. Predicting firefighters' exertion based on machine learning techniques.
  20. HE, B., MITROVIC-MINIC, S. N. E. Z. A. N. A., GARIS, L., ROBINSON, P., & STEPHEN, T. (2019). Hiring schedule optimization at the surrey fire department.
  21. Lian, X., Melancon, S., Presta, J. R., Reevesman, A., Spiering, B., & Woodbridge, D. (2019, August). Scalable Real-time Prediction and Analysis of San Francisco Fire Department Response Times. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 694-699). IEEE.
  22. Wu, X., Dunne, R., Zhang, Q., & Shi, W. (2017, October). Edge computing enabled smart firefighting: opportunities and challenges. In Proceedings of the Fifth ACM/IEEE Workshop on Hot Topics in Web Systems and Technologies (pp. 1-6).
  23. Pluntke, U., Gerke, S., Sridhar, A., Weiss, J., & Michel, B. (2019, July). Evaluation and classification of physical and psychological stress in firefighters using heart rate variability. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2207-2212). IEEE.
  24. Hamke, E. E., Martinez-Ramon, M., Nafchi, A. R., & Jordan, R. (2019). Detecting breathing rates and depth of breath using LPCs and Restricted Boltzmann Machines. Biomedical Signal Processing and Control, 48, 1-11. https://doi.org/10.1016/j.bspc.2018.09.009
  25. Hadad, D., Al Masry, Z., Nicod, J. M., Varnier, C., &Zerhouni, N. (2019, May). A Predictive Decision Approach for Firefighters Using Variable Selection Technique. In 2019 Prognostics and System Health Management Conference (PHM-Paris) (pp. 258-263). IEEE.
  26. Abdolmaleki, A., Movahedi, M., Lau, N., & Reis, L. P. (2012, June). A distributed cooperative reinforcement learning method for decision making in Fire Brigade Teams. In Robot Soccer World Cup (pp. 237-248). Springer, Berlin, Heidelberg.
  27. Couchot, J. F., Guyeux, C., & Royer, G. (2019, June). Anonymously forecasting the number and nature of firefighting operations. In Proceedings of the 23rd International Database Applications & Engineering Symposium (pp. 1-8).
  28. Nahuis, S. L. C., Guyeux, C., Arcolezi, H. H., Couturier, R., Royer, G., &Lotufo, A. D. P. (2019, April). Long short-term memory for predicting firemen interventions. In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1132-1137). IEEE.
  29. Guyeux, C., Nicod, J. M., Varnier, C., Al Masry, Z., Zerhouny, N., Omri, N., & Royer, G. (2019, September). Firemen prediction by using neural networks: A real case study. In Proceedings of SAI Intelligent Systems Conference (pp. 541-552). Springer, Cham.
  30. Mandal, S., Annaheim, S., Greve, J., Camenzind, M., & Rossi, R. M. (2019). Modeling for predicting the thermal protective and thermo-physiological comfort performance of fabrics used in firefighters' clothing. Textile Research Journal, 89(14), 2836-2849. https://doi.org/10.1177/0040517518803779
  31. Jaradat, F., & Valles, D. (2018, December). A Human Detection Approach for Burning Building Sites Using Deep Learning Techniques. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1434-1435). IEEE.
  32. Weidinger, Julian, Sebastian Schlauderer, and Sven Overhage. "Information Technology to the Rescue? Explaining the Acceptance of Emergency Response Information Systems by Firefighters." IEEE Transactions on Engineering Management (2021).
  33. Shaikh, Mohammad K. Cue-centric model of the fireground incident commander's decision making process. Diss. Loughborough University, 2011.
  34. Klein, Gary A. "A recognition-primed decision (RPD) model of rapid decision making." Decision making in action: Models and methods 5.4 (1993): 138-147.