Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network

Research Article
Open access

Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network

Haoyu Tan 1*
  • 1 Concordia University,1455 De Maisonneuve Blvd. W. Montreal, QC, H3G 1M8, CANADA    
  • *corresponding author t_haoyu@live.concordia.ca
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/34/20230301
ACE Vol.34
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-293-0
ISBN (Online): 978-1-83558-294-7

Abstract

Automotive fire robots that are used in factories can carry out diverse operations in regard to patrolling, fire detection, and programmed fire rescue. An accurate detection of fire sources in factories is significantly crucial for unmanned firefighting robot in terms of building a reliable sensor system. An approach proposed by this paper to recognize the color and dynamic shape of varying flames based on HSV color algorithms and Convolutional Neural Network. As a comparison to traditional RGB image processing, this approach is more efficient in isolating colors in environment and more adaptive to a fire site that includes multiple noise factors. The research in this paper uses image processing algorithms that is trained by CNN to detect flames in simulated factory environments, followed by a HSV color locating algorithm to compute the coordinates of target fire to perform inverse kinematic analysis on an unmanned firefighting robot.

Keywords:

robotics, firefighting robot, fire detection, image processing, deep learning

Tan,H. (2024). Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network. Applied and Computational Engineering,34,68-75.
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References

[1]. S Campbell R., Fires in U.S. Industrial or Manufacturing Properties (2018), National Fire Protection Association

[2]. Z. Zhang and Y. Hu, "Video Smoke Detection Based on Convolution Neural Network", 2017 International Conference on Computer Technology Electronics and Communication (ICCTEC)

[3]. A. Q. Nguyen, H. T. Nguyen, V. C. Tran, H. X. Pham and J. Pestana, "A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance," 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam, 2021, pp. 338-343, doi: 10.1109/ICCE48956.2021.9352080

[4]. Li, P., & Zhao, W. (2020). Image fire detection algorithms based on Convolutional Neural Networks. Case Studies in Thermal Engineering, 19, 100625. https://doi.org/10.1016/j.csite.2020.100625

[5]. K. Muhammad, J. Ahmad, I. Mehmood, S. Rho and S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," in IEEE Access, vol. 6, pp. 18174-18183, 2018, doi: 10.1109/ACCESS.2018.2812835

[6]. J. Suresh, "Fire-fighting robot," 2017 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2017, pp. 1-4, doi: 10.1109/ICCIDS.2017.8272649

[7]. S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186

[8]. Muhammad, K., Ahmad, J., & Baik, S. W. (2018). Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing, 288, 30–42. https://doi.org/10.1016/j.neucom.2017.04.083

[9]. Zhang C, Alexander BJ, Stephens ML, Lambert MF, Gong J. A convolutional neural network for pipe crack and leak detection in smart water network. Structural Health Monitoring. 2023;22(1):232-244. doi:10.1177/14759217221080198

[10]. Seo, S.-K., Yoon, Y.-G., Lee, J., Na, J., & Lee, C.-J. (2022). Deep Neural Network-based optimization framework for safety evacuation route during toxic gas leak incidents. Reliability Engineering & System Safety, 218, 108102. https://doi.org/10.1016/j.ress.2021.108102

[11]. Google. (n.d.). Advanced guide to inception V3. Google. https://cloud.google.com/tpu/docs/inception-v3-advanced

[12]. Choe, J., & Shim, H. (2019). Attention-based dropout layer for weakly supervised object localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2219-2228)

Cite this article

Tan,H. (2024). Research on real-time fire detection and locating for automotive firefighting robot in factories based on Convolutional Neural Network. Applied and Computational Engineering,34,68-75.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-293-0(Print) / 978-1-83558-294-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.34
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. S Campbell R., Fires in U.S. Industrial or Manufacturing Properties (2018), National Fire Protection Association

[2]. Z. Zhang and Y. Hu, "Video Smoke Detection Based on Convolution Neural Network", 2017 International Conference on Computer Technology Electronics and Communication (ICCTEC)

[3]. A. Q. Nguyen, H. T. Nguyen, V. C. Tran, H. X. Pham and J. Pestana, "A Visual Real-time Fire Detection using Single Shot MultiBox Detector for UAV-based Fire Surveillance," 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam, 2021, pp. 338-343, doi: 10.1109/ICCE48956.2021.9352080

[4]. Li, P., & Zhao, W. (2020). Image fire detection algorithms based on Convolutional Neural Networks. Case Studies in Thermal Engineering, 19, 100625. https://doi.org/10.1016/j.csite.2020.100625

[5]. K. Muhammad, J. Ahmad, I. Mehmood, S. Rho and S. W. Baik, "Convolutional Neural Networks Based Fire Detection in Surveillance Videos," in IEEE Access, vol. 6, pp. 18174-18183, 2018, doi: 10.1109/ACCESS.2018.2812835

[6]. J. Suresh, "Fire-fighting robot," 2017 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2017, pp. 1-4, doi: 10.1109/ICCIDS.2017.8272649

[7]. S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, 2017, pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186

[8]. Muhammad, K., Ahmad, J., & Baik, S. W. (2018). Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing, 288, 30–42. https://doi.org/10.1016/j.neucom.2017.04.083

[9]. Zhang C, Alexander BJ, Stephens ML, Lambert MF, Gong J. A convolutional neural network for pipe crack and leak detection in smart water network. Structural Health Monitoring. 2023;22(1):232-244. doi:10.1177/14759217221080198

[10]. Seo, S.-K., Yoon, Y.-G., Lee, J., Na, J., & Lee, C.-J. (2022). Deep Neural Network-based optimization framework for safety evacuation route during toxic gas leak incidents. Reliability Engineering & System Safety, 218, 108102. https://doi.org/10.1016/j.ress.2021.108102

[11]. Google. (n.d.). Advanced guide to inception V3. Google. https://cloud.google.com/tpu/docs/inception-v3-advanced

[12]. Choe, J., & Shim, H. (2019). Attention-based dropout layer for weakly supervised object localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2219-2228)

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