AI-Powered Fire Detection System for Accurate and Timely Emergency Response

Year : 2024 | Volume : 02 | Issue : 02 | Page : 30 36
    By

    Umesh Bhamare,

  • Smita Badarkhe,

  • Parth Rathi,

  • Rishikesh Pandey,

  1. Student, Department of Electronics & Telecommunication Engineering , SKN College of Engineering, Savitribai Phule Pune University, (SPPU), Pune, Maharashtra, India
  2. Student, Department of Electronics & Telecommunication Engineering , SKN College of Engineering, Savitribai Phule Pune University, (SPPU), Pune, Maharashtra, India
  3. Student, Department of Electronics & Telecommunication Engineering , SKN College of Engineering, Savitribai Phule Pune University, (SPPU), Pune, Maharashtra, India
  4. Student, Department of Electronics & Telecommunication Engineering , SKN College of Engineering, Savitribai Phule Pune University, (SPPU), Pune, Maharashtra, India

Abstract

AI-based fire detection system leverages deep learning and Open CV. Issues with conventional fire detection techniques include false alarms and expense. The proposed system uses deep learning for real-time fire detection in videos, enhancing accuracy and adaptability. OpenCV aids in crowd counting, improving situational awareness during emergencies. This innovation promises to revolutionize fire safety by offering timely and precise detection, potentially saving lives and property. Additionally, by counting crowds, the method increases emergency safety awareness. It helps safeguard individuals and property by sending out prompt and precise alerts. It expedites reactions and lowers false alarms in comparison to older systems. Deep learning is used to improve accuracy and flexibility in various contexts by enabling the system to identify fires in real-time through video data. Moreover, OpenCV is employed for population counting, which improves situational awareness in emergency situations. This AI technology can be applied in industries, homes, and workplaces to improve and automate fire safety. AI-based fire alert system demonstrates significant improvements over traditional methods by reducing response times, minimizing false alarms, and enhancing overall safety. Its adaptability and scalability make it suitable for various environments, including residential buildings, commercial complexes and industrial facilities.

Keywords: Fire detection, deep learning, open cv, real-time detection, crowd counting

[This article belongs to International Journal of Optical Innovations & Research ]

How to cite this article:
Umesh Bhamare, Smita Badarkhe, Parth Rathi, Rishikesh Pandey. AI-Powered Fire Detection System for Accurate and Timely Emergency Response. International Journal of Optical Innovations & Research. 2024; 02(02):30-36.
How to cite this URL:
Umesh Bhamare, Smita Badarkhe, Parth Rathi, Rishikesh Pandey. AI-Powered Fire Detection System for Accurate and Timely Emergency Response. International Journal of Optical Innovations & Research. 2024; 02(02):30-36. Available from: https://journals.stmjournals.com/ijoir/article=2024/view=190077


References

  1. Cheng G, Chen X, Wang C, Li X, Xian B, Yu H. Visual fire detection using deep learning: A survey. Neurocomputing. 2024 May 31; 596: 127975.
  2. Debnath S, Gautam J, Rai SK. IoT Based Smart Home and Office Fire Notification Alert System. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). 2024 Mar 14; 2: 1–5.
  3. Razmi SM, Saad N, Asirvadam Vision-based flame detection: motion detection & fire analysis. In 2010 IEEE Student Conference on Research and Development (SCOReD). 2010 Dec 13; 187– 191.
  4. Ligang M, Yanjun C, Aizhong Flame region detection using color and motion features in video sequences. In The 26th IEEE Chinese Control and Decision Conference (2014 CCDC). 2014 May 31; 3005–3009.
  5. Toulouse T, Rossi L, Celik T, Akhloufi Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods. Signal Image Video Process. 2016 Apr; 10(4): 647–54.
  6. Na YM, Hyun DH, Park DH, Hwang SH, Lee SH. AI Fire Detection & Notification System. J Korea Soc Comput Inform. 2020; 25(12): 63–71.
  7. Birajdar GS, Baz M, Singh R, Rashid M, Gehlot A, Akram SV, Alshamrani SS, AlGhamdi AS. Realization of people density and smoke flow in buildings during fire accidents using raspberry and openCV. Sustainability. 2021 Oct 7; 13(19): 11082.
  8. Sathyakala G, Kirthika V, Aishwarya B. Computer vision-based fire detection with a video alert system. In 2018 IEEE International Conference on Communication and Signal Processing (ICCSP). 2018 Apr 3; 0725–0727.
  9. Obulesu O, Hanshika A, Lahari A, Arunodaya P, Ashwini People Counting and Tracking System in Real-Time Using Deep Learning Techniques. Int J Res Appl Sci Eng Technol. 2023; 11(7): 713– 720. ISSN: 2321-9653, https://doi.org/10.22214/ijraset.2023.54182
  10. Liu W, Salzmann M, Fua P. Context-aware crowd counting. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019; 5099–5108.
  11. Sindagi VA, Patel VM. HA-CCN: Hierarchical attention-based crowd counting network. IEEE Trans Image Process. 2019 Jul 19; 29: 323–35.
  12. Shen Z, Xu Y, Ni B, Wang M, Hu J, Yang X. Crowd counting via adversarial cross-scale consistency pursuit. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2018; 5245–5254.
  13. Zhao T, Nevatia R, Wu Segmentation and tracking of multiple humans in crowded environments. IEEE Trans Pattern Anal Mach Intell. 2008 Jun 6; 30(7): 1198–211.
Regular Issue Subscription Review Article
Volume 02
Issue 02
Received 18/07/2024
Accepted 12/12/2024
Published 23/12/2024
351

Login


My IP

PlumX Metrics