Abstract
Firefighting is a dangerous activity that puts firefighters into conditions that threaten their safety in order to save lives. To reduce firefighter injuries and limit their exposure to hazardous conditions, a range of technologies have been developed to improve their planning, situational awareness, and firefighting activities. These technologies allow firefighters to be more intelligent about their activities to reduce the likelihood of injury and be more effective. This chapter provides an overview of technologies currently being used as well as those being developed to support more intelligent firefighting. This includes monitoring devices, imaging systems, and robotic platforms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R. Campbell and B. Evarts, “United States Firefighter Injuries in 2019,” NFPA Res. Rep., no. November, p. 15, 2020.
R. F. Fahy, J. T. Petrolli, and J. L. Molis, “Firefighter Fatalities in the US-2019,” NFPA Res. Rep., no. June, pp. 1–26, 2019.
C. Grant, A. Hamins, N. Bryner, A. Jones, and G. Koepke, “Research Roadmap for Smart Fire Fighting,” Summ. Report, NIST Spec. Publ. 1191, p. 247, 2015.
G. Marbach, M. Loepfe, and T. Brupbacher, “An image processing technique for fire detection in video images,” Fire Saf. J., vol. 41, no. 4, pp. 285–289, 2006.
K. Muhammad, J. Ahmad, I. Mehmood, S. Rho, and S. W. Baik, “Convolutional Neural Networks Based Fire Detection in Surveillance Videos,” IEEE Access, vol. 6, pp. 18174–18183, 2018.
A. E. Çetin et al., “Video fire detection - Review,” Digit. Signal Process. A Rev. J., vol. 23, no. 6, pp. 1827–1843, 2013.
J. L. Hodges, B. Y. Lattimer, and K. D. Luxbacher, “Compartment fire predictions using transpose convolutional neural networks,” Fire Saf. J., vol. 108, no. November 2018, 2019.
W. C. Tam et al., “Generating Synthetic Sensor Data to Facilitate Machine Learning Paradigm for Prediction of Building Fire Hazard,” Fire Technol., 2020.
B. Y. Lattimer, J. L. Hodges, A. M. Lattimer, “Using Machine Learning in Physics-based Simulation of Fire,” Fire Saf. J., 2020.
FLIR, “Packbot - Robots: Your Guid to the World of Robotics,” IEEE, 2020.
G. Lab, “x-RHex - Robots : Your Guid to the World of Robotics,” IEEE, 2020.
B. Dynamics, “BigDog - Robots: Your Guide to the World of Robotics,” IEEE, 2020.
J. W. Starr and B. Y. Lattimer, “Application of thermal infrared stereo vision in fire environments,” 2013 IEEE/ASME Int. Conf. Adv. Intell. Mechatronics Mechatronics Hum. Wellbeing, AIM 2013, pp. 1675–1680, 2013.
J. W. Starr and B. Y. Lattimer, “A comparison of IR stereo vision and LIDAR for use in fire environments,” Proc. IEEE Sensors, pp. 3–6, 2012.
J. W. Starr and B. Y. Lattimer, “Evaluation of Navigation Sensors in Fire Smoke Environments,” Fire Technol., vol. 50, no. 6, pp. 1459–1481, 2014.
J. H. Kim, S. Jo, and B. Y. Lattimer, “Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images,” J. Sensors, vol. 2016, 2016.
J.-H. Kim, Y. Sung, and B. Y. Lattimer, “Bayesian estimation based real-time fire-heading in smoke-filled indoor environments using thermal imagery,” in Proceedings - IEEE International Conference on Robotics and Automation, 2017.
J. H. Kim and B. Y. Lattimer, “Real-time probabilistic classification of fire and smoke using thermal imagery for intelligent firefighting robot,” Fire Saf. J., vol. 72, pp. 40–49, 2015.
N. Kerle, F. Nex, M. Gerke, D. Duarte, and A. Vetrivel, “UAV-based structural damage mapping: A review,” ISPRS Int. J. Geo-Information, vol. 9, no. 1, pp. 1–23, 2019.
F. Nex, D. Duarte, A. Steenbeek, and N. Kerle, “Towards real-time building damage mapping with low-cost UAV solutions,” Remote Sens., vol. 11, no. 3, pp. 1–14, 2019.
N. 2400, “Standard for Small Unmanned Aircraft Systems (sUAS) Used for Public Safety Operations,” NFPA, 2019.
T. Chung, “DARPA Subterranean (SubT) Challenge,” https://www.darpa.mil/program/darpa-subterranean-challenge, 2018.
A. Fonseca, T. S. Mayor, and J. B. L. M. Campos, “Guidelines for the specification of a PCM layer in firefighting protective clothing ensembles,” Appl. Therm. Eng., vol. 133, no. March 2017, pp. 81–96, 2018.
H. L. Phelps, S. D. Watt, H. S. Sidhu, and L. A. Sidhu, “Using Phase Change Materials and Air Gaps in Designing Fire Fighting Suits: A Mathematical Investigation,” Fire Technol., vol. 55, no. 1, pp. 363–381, 2019.
D. Dias and J. P. S. Cunha, “Wearable health devices—vital sign monitoring, systems and technologies,” Sensors (Switzerland), vol. 18, no. 8, 2018.
S. Rodrigues, J. S. Paiva, D. Dias, G. Pimentel, M. Kaiseler, and J. P. S. Cunha, “Wearable Biomonitoring Platform for the Assessment of Stress and its Impact on Cognitive Performance of Firefighters: An Experimental Study,” Clin. Pract. Epidemiol. Ment. Heal., vol. 14, no. 1, pp. 250–262, 2018.
Z. Deng, Y. Yu, D. Zou, W. Guan, and L. Yang, “OPTIMIZATION AND IMPLEMENTATION OF A THERMOACOUSTIC FLASHOVER DETECTOR,” MS Thesis, Univ. Maryland, Dep. Fire Prot. Eng., vol. 6, no. 3, 2013.
K. Yun, J. Bustos, and T. Lu, “Predicting rapid fire growth (flashover) using conditional generative adversarial networks,” arXiv, pp. 10–13, 2018.
B. Lattimer et al., “Humanoid Firefighting Robot for Structure Fires,” Interflam 2016, 2016.
T. Chen, H. Yuan, G. Su, and W. Fan, “An automatic fire searching and suppression system for large spaces,” Fire Saf. J., vol. 39, pp. 297–307, 2004.
C. Yuan, Y. Zhang, and Z. Liu, “A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques,” Can. J. For. Res., vol. 45, no. 7, pp. 783–792, 2015.
A. De Santis, B. Siciliano, and L. Villani, “A unified fuzzy logic approach to trajectory planning and inverse kinematics for a fire fighting robot operating in tunnels,” Intell. Serv. Robot., vol. 1, no. 1, pp. 41–49, 2008.
Y. Xin, K. Burchesky, J. de Vries, H. Magistrale, X. Zhou, and S. D’Aniello, “SMART Sprinkler Protection for Highly Challenging Fires—Part 2: Full-Scale Fire Tests in Rack Storage,” Fire Technol., vol. 53, no. 5, pp. 1885–1906, 2017.
Y. Xin, K. Burchesky, J. de Vries, H. Magistrale, X. Zhou, and S. D’Aniello, SMART Sprinkler Protection for Highly Challenging Fires—Part 1: System Design and Function Evaluation, vol. 53, no. 5. Springer US, 2017.
R. James, “Fire Fighting News: Automatic Fire Monitor Systems for Recycling and Water to Energy Plants,” Unifire.com, 2020.
G. Malik, “What if your sprinkler system could do think?,” Sprink. Age, 2018.
P. Liljebäck, Ø. Stavdahl, and A. Beitnes, “SnakeFighter - Development of a water hydraulic fire fighting snake robot,” 9th Int. Conf. Control. Autom. Robot. Vision, 2006, ICARCV ’06, no. 7465, 2006.
J. Hong, B. Min, J. Taylor, V. Raskin, and E. Matson, “NL-Based Communication with Firefighting Robots,” 2012 IEE Int. Conf. Syst. Man, Cybern. Oct. 14-27, COEX, Seoul, Korea, pp. 1461–1466, 2012.
P. Strauss, “Using Drones to Fight Hi-Rise Fires,” Technabob, vol. April, 2020.
J. G. McNeil and B. Y. Lattimer, “Robotic Fire Suppression Through Autonomous Feedback Control,” Fire Technol., vol. 53, no. 3, pp. 1171–1199, 2017.
J. G. McNeil and B. Y. Lattimer, “Autonomous Fire Suppression System for Use in High and Low Visibility Environments by Visual Servoing,” Fire Technol., vol. 52, no. 5, pp. 1343–1368, 2016.
T. Kevan, “Exoskeletons on the Move,” Digit. Eng., vol. December, 2018.
E. Kulisch, “Exoskeleton Could Give Delta Cargo Workers Superhuman Strength (with video),” Air Cargo, vol. January, 2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lattimer, B.Y., Hodges, J.L. (2022). Intelligent Firefighting. In: Naser, M., Corbett, G. (eds) Handbook of Cognitive and Autonomous Systems for Fire Resilient Infrastructures. Springer, Cham. https://doi.org/10.1007/978-3-030-98685-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-98685-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98684-1
Online ISBN: 978-3-030-98685-8
eBook Packages: Computer ScienceComputer Science (R0)