System For Real Time Fire And Smoke Intensity Detection
Main Article Content
Abstract
Fire poses a significant threat to daily life, causing both economic and social harm. To
mitigate these damages, early detection of fire and smoke is crucial, and this paper introduces a model
employing vision-based techniques. The proposed model utilizes image processing and convolutional
neural networks to detect fire and smoke, providing insights into their intensity and any changes in a
video. The model comprises two units for fire and smoke detection, each employing image preprocessing
techniques, including rule-based color detection and motion detection, along with CNN. The calculated
percentages of fire and smoke in the processed images offer detailed information about the severity of
the hazards in a specific area. The model detects whether the intensity of fire and smoke is increasing,
decreasing or constant.
Article Details
Upon receipt of accepted manuscripts, authors will be invited to complete a copyright license to publish the paper. At least the corresponding author must send the copyright form signed for publication. It is a condition of publication that authors grant an exclusive licence to the the INFOCOMP Journal of Computer Science. This ensures that requests from third parties to reproduce articles are handled efficiently and consistently and will also allow the article to be as widely disseminated as possible. In assigning the copyright license, authors may use their own material in other publications and ensure that the INFOCOMP Journal of Computer Science is acknowledged as the original publication place.
References
Affonso, E. T., Nunes, R. D., Rosa, R. L., Pivaro,
G. F., and Rodriguez, D. Z. Speech quality assessment
in wireless voip communication using deep
belief network. IEEE Access, 6:77022–77032,
Affonso, E. T., Rosa, R. L., and Rodriguez, D. Z.
Speech quality assessment over lossy transmission
channels using deep belief networks. IEEE
Signal Processing Letters, 25(1):70–74, 2017.
Barmpoutis, P., Dimitropoulos, K., Kaza, K., and
Grammalidis, N. Fire detection from images using
INFOCOMP, v. 23, no. 1, p. pp-pp, June, 2024.
Maheshkar et al. System For Real Time Fire And Smoke Intensity Detection 12
faster r-cnn and multidimensional texture analysis.
In ICASSP 2019-2019 IEEE International
Conference on Acoustics, Speech and Signal Processing
(ICASSP), pages 8301–8305. IEEE, 2019.
Bochkovskiy, A.,Wang, C.-Y., and Liao, H.-Y. M.
Yolov4: Optimal speed and accuracy of object detection.
arXiv preprint arXiv:2004.10934, 2020.
Borges, P. V. K. and Izquierdo, E. A probabilistic
approach for vision-based fire detection in
videos. IEEE transactions on circuits and systems
for video technology, 20(5):721–731, 2010.
Çelik, T., Özkaramanlı, H., and Demirel, H. Fire
and smoke detection without sensors: Image processing
based approach. In 2007 15th European
signal processing conference, pages 1794–1798.
IEEE, 2007.
Cetin, E. Computer vision based fire detection
dataset. Online [Accessed 17 March 2022]
http://signal. ee. bilkent. edu. tr/VisiFire/Ultimate
chase. Online [Accessed 17 March 2022]
http://ultimatechase. com, 2015.
Chen, T.-H.,Wu, P.-H., and Chiou, Y.-C. An early
fire-detection method based on image processing.
In 2004 International Conference on Image Processing,
ICIP ’04., volume 3, pages 1707–
Vol. 3, 2004.
Chunyu, Y., Jun, F., Jinjun, W., and Yongming, Z.
Video fire smoke detection using motion and color
features. Fire Technology, 46:651–663, 07 2010.
Dantas Nunes, R., Lopes Rosa, R., and Zegarra
Rodríguez, D. Performance improvement of
a non-intrusive voice quality metric in lossy networks.
IET Communications, 13(20):3401–3408,
de Almeida, F. L., Rosa, R. L., and Rodriguez,
D. Z. Voice quality assessment in communication
services using deep learning. In 2018 15th International
Symposium on Wireless Communication
Systems (ISWCS), pages 1–6. IEEE, 2018.
dos Santos, M. R., Batista, A. P., Rosa, R. L.,
Saadi, M., Melgarejo, D. C., and Rodríguez,
D. Z. Asqm: Audio streaming quality metric
based on network impairments and user preferences.
IEEE Transactions on Consumer Electronics,
(3):408–420, 2023.
Dos Santos, M. R., Rodriguez, D. Z., and Rosa,
R. L. A novel qoe indicator for mobile networks
based on twitter opinion ranking. In 2023 International
Conference on Software, Telecommunications
and Computer Networks (SoftCOM), pages
–6. IEEE, 2023.
Foggia, P., Saggese, A., and Vento, M. Realtime
fire detection for video-surveillance applications
using a combination of experts based on
color, shape, and motion. IEEE TRANSACTIONS
on circuits and systems for video technology,
(9):1545–1556, 2015.
Gagliardi, A., de Gioia, F., and Saponara, S. A
real-time video smoke detection algorithm based
on kalman filter and cnn. Journal of Real-Time
Image Processing, pages 1–11, 2021.
Grammalidis, N., Dimitropoulos, K., and Cetin,
E. Firesense database of videos for flame and
smoke detection. IEEE Trans Circuits Syst Video
Technol, 25:339–351, 2017.
Hongyu, H., Ping, K., Li, F., and Huaxin, S. An
improved multi-scale fire detection method based
on convolutional neural network. In 2020 17th International
Computer Conference on Wavelet Active
Media Technology and Information Processing
(ICCWAMTIP), pages 109–112. IEEE, 2020.
Iandola, F. N., Han, S., Moskewicz, M. W.,
Ashraf, K., Dally, W. J., and Keutzer, K.
Squeezenet: Alexnet-level accuracy with 50x
fewer parameters and< 0.5 mb model size. arXiv
preprint arXiv:1602.07360, 2016.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.
Imagenet classification with deep convolutional
neural networks. Advances in neural information
processing systems, 25, 2012.
Labati, R. D., Genovese, A., Piuri, V., and Scotti,
F. Wildfire smoke detection using computational
intelligence techniques enhanced with synthetic
smoke plume generation. IEEE Transactions
on Systems, Man, and Cybernetics: Systems,
:1003–1012, 2013.
Li, P. and Zhao, W. Image fire detection algorithms
based on convolutional neural networks.
Case Studies in Thermal Engineering, 19:100625,
Matukhina, O., Amaeva, L., and Merzlyakov, S.
Fire detection system with utilization of industrial
INFOCOMP, v. 23, no. 1, p. pp-pp, June, 2024.
Maheshkar et al. System For Real Time Fire And Smoke Intensity Detection 13
video surveillance system. In 2020 International
Multi-Conference on Industrial Engineering and
Modern Technologies (FarEastCon), pages 1–5.
IEEE, 2020.
Muhammad, K., Ahmad, J., and Baik, S.W. Early
fire detection using convolutional neural networks
during surveillance for effective disaster management.
Neurocomputing, 288:30–42, 2018.
Muhammad, K., Ahmad, J., Lv, Z., Bellavista,
P., Yang, P., and Baik, S. W. Efficient
deep cnn-based fire detection and localization in
video surveillance applications. IEEE Transactions
on Systems, Man, and Cybernetics: Systems,
(7):1419–1434, 2018.
Muhammad, K., Ahmad, J., Mehmood, I., Rho,
S., and Baik, S.W. Convolutional neural networks
based fire detection in surveillance videos. Ieee
Access, 6:18174–18183, 2018.
Muhammad, K., Khan, S., Elhoseny, M., Ahmed,
S. H., and Baik, S. W. Efficient fire detection for
uncertain surveillance environment. IEEE Transactions
on Industrial Informatics, 15(5):3113–
, 2019.
Okey, O. D., Udo, E. U., Rosa, R. L., Rodríguez,
D. Z., and Kleinschmidt, J. H. Investigating chatgpt
and cybersecurity: A perspective on topic
modeling and sentiment analysis. Computers &
Security, 135:103476, 2023.
Redmon, J. and Farhadi, A. Yolov3: An
incremental improvement. arXiv preprint
arXiv:1804.02767, 2018.
Ren, S., He, K., Girshick, R., and Sun, J. Faster
r-cnn: Towards real-time object detection with region
proposal networks. Advances in neural information
processing systems, 28, 2015.
Rezaie, V., Parnianifard, A., Rodriguez, D., Mumtaz,
S., and Wuttisittikulkij, L. Speech emotion
recognition using anfis and pso-optimization with
word2vec. J Neuro Spine, 1(1):41–56, 2023.
Rodriguez, D. Z. and Bressan, G. Video quality
assessments on digital tv and video streaming services
using objective metrics. IEEE Latin America
Transactions, 10(1):1184–1189, 2012.
Rodriguez, D. Z. and Junior, L. C. B. Determining
a non-intrusive voice quality model using machine
learning and signal analysis in time. INFOCOMP
Journal of Computer Science, 18(2), 2019.
Rodríguez, D. Z., Rosa, R. L., Almeida, F. L., Mittag,
G., and Möller, S. Speech quality assessment
in wireless communications with mimo systems
using a parametric model. IEEE Access, 7:35719–
, 2019.
Sandler, M., Howard, A., Zhu, M., Zhmoginov,
A., and Chen, L.-C. Mobilenetv2: Inverted residuals
and linear bottlenecks. In Proceedings of the
IEEE conference on computer vision and pattern
recognition, pages 4510–4520, 2018.
Silva, D. H., Maziero, E. G., Saadi, M., Rosa,
R. L., Silva, J. C., Rodriguez, D. Z., and Igorevich,
K. K. Big data analytics for critical information
classification in online social networks using
classifier chains. Peer-to-Peer Networking and
Applications, 15(1):626–641, 2022.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi,
A. Inception-v4, inception-resnet and the impact
of residual connections on learning. In Proceedings
of the AAAI conference on artificial intelligence,
volume 31, 2017.
Vijayalakshmi, S. and Muruganand, S. Smoke detection
in video images using background subtraction
method for early fire alarm system. In 2017
nd International Conference on Communication
and Electronics Systems (ICCES), pages 167–171.
IEEE, 2017.
Zhang, X., Qian, K., Jing, K., Yang, J., and Yu, H.
Fire detection based on convolutional neural networks
with channel attention. In 2020 Chinese
Automation