System For Real Time Fire And Smoke Intensity Detection

Main Article Content

Vikas Maheshkar Vikas Maheshkar
Ayush Singh
Huny Dahiya


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

How to Cite
Vikas Maheshkar, V. M., Ayush Singh, Huny Dahiya, & Vansh. (2024). System For Real Time Fire And Smoke Intensity Detection. INFOCOMP Journal of Computer Science, 23(1). Retrieved from
Computer Graphics, Image Processing, Visualization and Virtual Reality


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