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Automated Fire Extinguishing System Using a Deep Learning Based Framework

Author

Listed:
  • Senthil Kumar Jagatheesaperumal

    (Department of Electronics & Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, Tamil Nadu, India)

  • Khan Muhammad

    (Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Republic of Korea)

  • Abdul Khader Jilani Saudagar

    (Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Joel J. P. C. Rodrigues

    (College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266555, China
    Instituto de Telecomunicações, 6201-001 Covilhã, Portugal)

Abstract

Fire accidents occur in every part of the world and cause a large number of casualties because of the risks involved in manually extinguishing the fire. In most cases, humans cannot detect and extinguish fire manually. Fire extinguishing robots with sophisticated functionalities are being rapidly developed nowadays, and most of these systems use fire sensors and detectors. However, they lack mechanisms for the early detection of fire, in case of casualties. To detect and prevent such fire accidents in its early stages, a deep learning-based automatic fire extinguishing mechanism was introduced in this work. Fire detection and human presence in fire locations were carried out using convolution neural networks (CNNs), configured to operate on the chosen fire dataset. For fire detection, a custom learning network was formed by tweaking the layer parameters of CNN for detecting fires with better accuracy. For human detection, Alex-net architecture was employed to detect the presence of humans in the fire accident zone. We experimented and analyzed the proposed model using various optimizers, activation functions, and learning rates, based on the accuracy and loss metrics generated for the chosen fire dataset. The best combination of neural network parameters was evaluated from the model configured with an Adam optimizer and softmax activation, driven with a learning rate of 0.001, providing better accuracy for the learning model. Finally, the experiments were tested using a mobile robotic system by configuring them in automatic and wireless control modes. In automatic mode, the robot was made to patrol around and monitor for fire casualties and fire accidents. It automatically extinguished the fire using the learned features triggered through the developed model.

Suggested Citation

  • Senthil Kumar Jagatheesaperumal & Khan Muhammad & Abdul Khader Jilani Saudagar & Joel J. P. C. Rodrigues, 2023. "Automated Fire Extinguishing System Using a Deep Learning Based Framework," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:608-:d:1046868
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    References listed on IDEAS

    as
    1. Xiaofei Lin & Shouxin Song & Huaiyuan Zhai & Pengwei Yuan & Mingli Chen, 2020. "Using catastrophe theory to analyze subway fire accidents," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 223-235, February.
    2. Aleksandr Smolin & Andrei Yamaev & Anastasia Ingacheva & Tatyana Shevtsova & Dmitriy Polevoy & Marina Chukalina & Dmitry Nikolaev & Vladimir Arlazarov, 2022. "Reprojection-Based Numerical Measure of Robustness for CT Reconstruction Neural Network Algorithms," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    3. Malka N. Halgamuge & Eshan Daminda & Ampalavanapillai Nirmalathas, 2020. "Best optimizer selection for predicting bushfire occurrences using deep learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 845-860, August.
    4. Jorge Pereira & Jérôme Mendes & Jorge S. S. Júnior & Carlos Viegas & João Ruivo Paulo, 2022. "A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration," Mathematics, MDPI, vol. 10(3), pages 1-19, January.
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