Fire detection for video surveillance applications using ICA K-medoids-based color model and efficient spatio-temporal visual features

An overview of statistical learning theory

Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,305 · see citing works

Region-based convolutional networks for accurate object detection and segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · see citing works

Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition

Atashpaz-Gargari, E., & Lucas, C. (2007) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2007.4425083 · Cited by 2,741 · see citing works

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504 · see citing works

PCANet: A Simple Deep Learning Baseline for Image Classification?

Chan, T.-H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2475625 · Cited by 1,598 · see citing works

Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library

Kaehler, A., & Bradski, G. R. (2016) · Cited by 1,211 · see citing works

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817 · see citing works

An early fire-detection method based on image processing

Chen, T.-H., Wu, P.-H., & Chiou, Y.-C. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1421401 · Cited by 669 · see citing works

Computer vision based method for real-time fire and flame detection

Töreyın, B. U., Dedeoğlu, Y., Güdükbay, U., & Çetin, A. E. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.06.015 · Cited by 640 · see citing works

Fire detection in video sequences using a generic color model

Çelik, T., & Demirel, H. (2008) · Fire Safety Journal · doi:10.1016/j.firesaf.2008.05.005 · Cited by 562 · see citing works

Early fire detection using convolutional neural networks during surveillance for effective disaster management

Muhammad, K., Ahmad, J., & Baik, S. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.04.083 · Cited by 558 · see citing works

Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018) · IEEE Access · doi:10.1109/access.2018.2812835 · Cited by 556 · see citing works

Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Muhammad, K., Ahmad, J., Lv, Z., Bellavista, P., Yang, P., & Baik, S. W. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2830099 · Cited by 539 · see citing works

Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion

Foggia, P., Saggese, A., & Vento, M. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2392531 · Cited by 483 · see citing works

Real-time forest fire detection with wireless sensor networks

Yu, L., Wang, N., & Meng, X. (2005) · International Conference on Wireless Communications, Networking and Mobile Computing · doi:10.1109/wcnm.2005.1544272 · Cited by 463 · see citing works

Fire detection based on vision sensor and support vector machines

Ko, B. C., Cheong, K., & Nam, J.-Y. (2008) · Fire Safety Journal · doi:10.1016/j.firesaf.2008.07.006 · Cited by 389 · see citing works

Fire detection using statistical color model in video sequences

Çelik, T., Demirel, H., Özkaramanlı, H., & Uyguroğlu, M. K. (2007) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2006.12.003 · Cited by 342 · see citing works

Video fire detection – Review

Çetin, A. E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboǧlu, Y. H., Töreyın, B. U., & Verstockt, S. (2013) · Digital Signal Processing · doi:10.1016/j.dsp.2013.07.003 · Cited by 302 · see citing works

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

Kantorov, V., Oquab, M., Cho, M., & Laptev, I. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_22 · Cited by 295 · see citing works

An image processing technique for fire detection in video images

Marbach, G., Loepfe, M., & Brupbacher, T. (2006) · Fire Safety Journal · doi:10.1016/j.firesaf.2006.02.001 · Cited by 276 · see citing works

Flame recognition in video

Phillips, W., Shah, M. A., & Lobo, N. D. V. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(01)00135-0 · Cited by 275 · see citing works

Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Dimitropoulos, K., Barmpoutis, P., & Grammalidis, N. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2339592 · Cited by 264 · see citing works

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Borges, P. V. K., & Izquierdo, E. (2010) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2010.2045813 · Cited by 236 · see citing works

Covariance matrix-based fire and flame detection method in video

Habiboǧlu, Y. H., Günay, O., & Çetin, A. E. (2011) · Machine Vision and Applications · doi:10.1007/s00138-011-0369-1 · Cited by 215 · see citing works

Flame detection in video using hidden Markov models

Töreyın, B. U., Dedeoğlu, Y., & Çetin, A. E. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530284 · Cited by 209 · see citing works

Computer vision for wildfire research: An evolving image dataset for processing and analysis

Toulouse, T., Rossi, L., Campana, A., Çelik, T., & Akhloufi, M. A. (2017) · Fire Safety Journal · doi:10.1016/j.firesaf.2017.06.012 · Cited by 202 · see citing works

Optical Flow Estimation for Flame Detection in Videos

Mueller, M. F., Karasev, P. A., Kolesov, I., & Tannenbaum, A. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2258353 · Cited by 201 · see citing works

A background subtraction algorithm for detecting and tracking vehicles

Mandellos, N. A., Keramitsoglou, I., & Kiranoudis, C. T. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.07.083 · Cited by 173 · see citing works

Background modeling methods in video analysis: A review and comparative evaluation

Xu, Y., Dong, J., Zhang, B., & Xu, D. (2016) · CAAI Transactions on Intelligence Technology · doi:10.1016/j.trit.2016.03.005 · Cited by 169 · see citing works

Experimentally Defined Convolutional Neural Network Architecture Variants for Non-Temporal Real-Time Fire Detection

Dunnings, A. J., & Breckon, T. P. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451657 · Cited by 150 · see citing works

Fast fire flame detection in surveillance video using logistic regression and temporal smoothing

Kong, S. G., Donglin, J., Li, S., & Kim, H. (2015) · Fire Safety Journal · doi:10.1016/j.firesaf.2015.11.015 · Cited by 130 · see citing works

Modeling and Formalization of Fuzzy Finite Automata for Detection of Irregular Fire Flames

Ko, B. C., Ham, S., & Nam, J. Y. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2157190 · Cited by 123 · see citing works

Video fire detection based on Gaussian Mixture Model and multi-color features

Han, X.-F., Jin, J. S., Wang, M., Jiang, W., Gao, L., & Xiao, L. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1102-y · Cited by 107 · see citing works

Fire flame detection in video sequences using multi-stage pattern recognition techniques

Truong, X. T., & Kim, J.-M. (2012) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2012.05.007 · Cited by 106 · see citing works

Fire and smoke detection using wavelet analysis and disorder characteristics

Rafiee, A., Dianat, R., Jamshidi, M., Tavakoli, R., & Asadollah, S. A. (2011) · International Conference on Computer Research and Development · doi:10.1109/iccrd.2011.5764295 · Cited by 101 · see citing works

A new PSO-based approach to fire flame detection using K-Medoids clustering

Khatami, A., Mirghasemi, S., Khosravi, A., Lim, C. P., & Nahavandi, S. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.09.021 · Cited by 99 · see citing works

Fire Pixel Classification using Fuzzy Logic and Statistical Color Model

Çelik, T., Ozkaramanlt, H., & Demirel, H. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366130 · Cited by 95 · see citing works

A Real-time Video Fire Flame and Smoke Detection Algorithm

Yu, C., Mei, Z., & Zhang, X. (2013) · Procedia Engineering · doi:10.1016/j.proeng.2013.08.140 · Cited by 91 · see citing works

Autonomous Fire-Detection System Using Adaptive Sensory Fusion for Intelligent Security Robot

Luo, R. C., & Su, K. L. (2007) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2007.897260 · Cited by 77 · see citing works

A computer vision-based method for fire detection in color videos

Qi, X., & Ebert, J. (2009) · Cited by 72 · see citing works

Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

Liu, Z., Yang, Y., & Ji, X. (2015) · Signal Image and Video Processing · doi:10.1007/s11760-014-0738-0 · Cited by 71 · see citing works

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

Hashemzadeh, M., & Farajzadeh, N. (2016) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2016.1237185 · Cited by 71 · see citing works

Video based wildfire detection at night

Günay, O., Taşdemir, K., Töreyın, B. U., & Çetin, A. E. (2009) · Fire Safety Journal · doi:10.1016/j.firesaf.2009.04.003 · Cited by 67 · see citing works

Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization

Karasulu, B., & Korukoğlu, S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.06.040 · Cited by 55 · see citing works

A non-temporal texture driven approach to real-time fire detection

Chenebert, A., Breckon, T. P., & Gaszczak, A. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115796 · Cited by 53 · see citing works

Picture processing grammar and its applications

Chang, S. (1971) · Information Sciences · doi:10.1016/s0020-0255(71)80002-6 · Cited by 53 · see citing works

RGB Color Model Based the Fire Detection Algorithm in Video Sequences on Wireless Sensor Network

Kim, Y.-H., Kim, A., & Jeong, H. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/923609 · Cited by 52 · see citing works

Vision-Based Fire Detection Algorithm Using Optical Flow

Ha, C., Hwang, U., Jeon, G., Cho, J., & Jeong, J. (2012) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2012.25 · Cited by 47 · see citing works

Adaptive flame detection using randomness testing and robust features

Wang, D.-C., Cui, X., Park, E., Jin, C., & Kim, H. (2012) · Fire Safety Journal · doi:10.1016/j.firesaf.2012.10.011 · Cited by 47 · see citing works

Fire flame detection based on GICA and target tracking

Rong, J., Zhou, D., Yao, W., Gao, W., Chen, J., & Wang, J. (2012) · Optics & Laser Technology · doi:10.1016/j.optlastec.2012.08.040 · Cited by 41 · see citing works

Combining keypoint-based and segment-based features for counting people in crowded scenes

Hashemzadeh, M., & Farajzadeh, N. (2016) · Information Sciences · doi:10.1016/j.ins.2016.01.060 · Cited by 40 · see citing works

Content-aware image resizing: An improved and shadow-preserving seam carving method

Hashemzadeh, M., Asheghi, B., & Farajzadeh, N. (2018) · Signal Processing · doi:10.1016/j.sigpro.2018.09.037 · Cited by 39 · see citing works

Exemplar-based facial expression recognition

Farajzadeh, N., & Hashemzadeh, M. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.057 · Cited by 38 · see citing works

Counting moving people in crowds using motion statistics of feature-points

Hashemzadeh, M., Pan, G., & Yao, M. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1367-2 · Cited by 37 · see citing works

Efficient Visual Fire Detection Applied For Video Retrieval

Borges, P., Izquierdo, E., & Mayer, J. (2008) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.41239 · Cited by 37 · see citing works

What is a visual language?

Erwig, M., Smeltzer, K., & Wang, X. (2016) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2016.10.005 · Cited by 33 · see citing works

Intelligent video surveillance beyond robust background modeling

Cermeño, E., Pérez, A., & Siguenza, J. A. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.08.052 · Cited by 33 · see citing works

Object-oriented convolutional features for fine-grained image retrieval in large surveillance datasets

Ahmad, J., Muhammad, K., Bakshi, S., & Baik, S. W. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.002 · Cited by 33 · see citing works

Visual language implementation through standard compiler–compiler techniques

Costagliola, G., Deufemia, V., & Polese, G. (2006) · Journal of Visual Languages & Computing · doi:10.1016/j.jvlc.2006.06.002 · Cited by 32 · see citing works

A Normalization Framework for Multimedia Databases

Chang, S.-K., Deufemia, V., Polese, G., & Vacca, M. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190651 · Cited by 31 · see citing works

ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised LocalizationPreprint

Kantorov, V., Oquab, M., Cho, M., & Ivan Laptev, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04331 · Cited by 28 · see citing works

Hiding information in videos using motion clues of feature points

Hashemzadeh, M. (2018) · Computers & electrical engineering · doi:10.1016/j.compeleceng.2018.03.046 · Cited by 27 · see citing works

Intelligent Space as a Framework for Fire Detection and Evacuation

Podržaj, P., & Hashimoto, H. (2007) · Fire Technology · doi:10.1007/s10694-007-0021-9 · Cited by 24 · see citing works

Towards a solid solution of real-time fire and flame detection

Jiang, B., Lu, Y., Li, X., & Lin, L. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2106-z · Cited by 22 · see citing works

Smart motion detection sensor based on video processing using self-organizing maps

Ortega-Zamorano, F., Molina‐Cabello, M. A., López‐Rubio, E., & Palomo, E. J. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.010 · Cited by 19 · see citing works

Detection of Multiple Dynamic Textures Using Feature Space Mapping

Rahman, A., & Murshed, M. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2017419 · Cited by 18 · see citing works

Robust Little Flame Detection on Real-Time Video Surveillance System

Lai, T. Y., Kuo, J.-Y., Fanjiang, Y., Ma, S., & Liao, Y. H. (2012) · International Conference on Innovations in Bio-inspired Computing and Applications · doi:10.1109/ibica.2012.41 · Cited by 15 · see citing works

Notice of Violation of IEEE Publication Principles - Effective visual fire detection in video sequences using probabilistic approach

Jenifer, P. (2011) · doi:10.1109/icetect.2011.5760241 · Cited by 13 · see citing works

COMBINING VELOCITY AND LOCATION-SPECIFIC SPATIAL CLUES IN TRAJECTORIES FOR COUNTING CROWDED MOVING OBJECTS

Hashemzadeh, M., PAN, G., Wang, Y., Yao, M., & Wu, J. F. (2013) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001413540037 · Cited by 11 · see citing works

Fast illumination-robust foreground detection using hierarchical distribution map for real-time video surveillance system

Son, J., Kim, S., & Sohn, K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.062 · Cited by 10 · see citing works

A fast and accurate moving object tracker in active camera model

Farajzadeh, N., Karamiani, A., & Hashemzadeh, M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4597-x · Cited by 9 · see citing works

Real-time record sensitive background classifier (RSBC)

Roy, S. M., & Ghosh, A. K. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.025 · Cited by 5 · see citing works

RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem

Soltanzadeh, P., & Hashemzadeh, M. (2020) · Information Sciences · doi:10.1016/j.ins.2020.07.014 · Cited by 223 · see citing works

A modified YOLOv5 architecture for efficient fire detection in smart cities

Yar, H., Khan, Z. A., Ullah, F. U. M., Ullah, W., & Baik, S. W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120465 · Cited by 140 · see citing works

A Survey of Video Surveillance Systems in Smart City

Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works

Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark

Yar, H., Hussain, T., Agarwal, M., Khan, Z. A., Gupta, S. K., & Baik, S. W. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3207006 · Cited by 118 · see citing works

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access · doi:10.1109/access.2019.2955387 · Cited by 114 · see citing works

A Real-Time Fire Detection Method from Video with Multifeature Fusion

Gong, F., Li, C., Gong, W., Li, X., Yuan, X., Ma, Y., & Song, T. (2019) · Computational Intelligence and Neuroscience · doi:10.1155/2019/1939171 · Cited by 90 · see citing works

CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation

Oskouei, A. G., Hashemzadeh, M., Asheghi, B., & Balafar, M. A. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108005 · Cited by 82 · see citing works

Randomly Initialized CNN with Densely Connected Stacked Autoencoder for Efficient Fire Detection

Khan, Z. A., Hussain, T., Ullah, F. U. M., Gupta, S. K., Lee, M. Y., & Baik, S. W. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105403 · Cited by 79 · see citing works

Development of early fire detection model for buildings using computer vision-based CCTV

Ahn, Y., Choi, H., & Kim, B. S. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.105647 · Cited by 78 · see citing works

UFS-Net: A unified flame and smoke detection method for early detection of fire in video surveillance applications using CNNs

Hosseini, A., Hashemzadeh, M., & Farajzadeh, N. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101638 · Cited by 73 · see citing works

Fire Sensor and Surveillance Camera-Based GTCNN for Fire Detection System

Sridhar, P., Thangavel, S. K., Parameswaran, L., & Oruganti, V. R. M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3244833 · Cited by 61 · see citing works

A hybrid method for fire detection based on spatial and temporal patterns

de Venâncio, P. V. A. B., Campos, R. J., Rezende, T. M., Lisboa, A. C., & Barbosa, A. V. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08260-2 · Cited by 56 · see citing works

QDL-CMFD: A Quality-independent and deep Learning-based Copy-Move image forgery detection method

Aria, M., Hashemzadeh, M., & Farajzadeh, N. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.017 · Cited by 53 · see citing works

Preferred vector machine for forest fire detection

Yang, X., Hua, Z., Zhang, L., Fan, X., Zhang, F., Ye, Q., & Fu, L. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109722 · Cited by 52 · see citing works

Fire Detection Based on Fractal Analysis and Spatio-Temporal Features

Torabian, M., Pourghassem, H., & Mahdavi‐Nasab, H. (2021) · Fire Technology · doi:10.1007/s10694-021-01129-7 · Cited by 42 · see citing works

A label-relevance multi-direction interaction network with enhanced deformable convolution for forest smoke recognition

Tao, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121383 · Cited by 42 · see citing works

A visualized fire detection method based on convolutional neural network beyond anchor

Li, Y., Zhang, W., Liu, Y., & Jin, Y. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03243-7 · Cited by 41 · see citing works

Smoke detection in video using convolutional neural networks and efficient spatio-temporal features

Hashemzadeh, M., Farajzadeh, N., & Heydari, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109496 · Cited by 39 · see citing works

PASS-CCTV: Proactive Anomaly surveillance system for CCTV footage analysis in adverse environmental conditions

Jeon, H., Kim, H., Kim, D., & Kim, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124391 · Cited by 38 · see citing works

A modified vision transformer architecture with scratch learning capabilities for effective fire detection

Yar, H., Khan, Z. A., Hussain, T., & Baik, S. W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123935 · Cited by 37 · see citing works

Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks

Muksimova, S., Umirzakova, S., Mardieva, S., & Cho, Y. I. (2021) · Sensors · doi:10.3390/s22010098 · Cited by 37 · see citing works

FKMAWCW: Categorical fuzzy k-modes clustering with automated attribute-weight and cluster-weight learning

Oskouei, A. G., Balafar, M. A., & Motamed, C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111494 · Cited by 35 · see citing works

Pixel-level automatic annotation for forest fire image

Yang, X., Chen, R., Zhang, F., Zhang, L., Fan, X., Ye, Q., & Fu, L. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104353 · Cited by 33 · see citing works

CGFFCM: A color image segmentation method based on cluster-weight and feature-weight learning

Oskouei, A. G., & Hashemzadeh, M. (2022) · Software Impacts · doi:10.1016/j.simpa.2022.100228 · Cited by 31 · see citing works

Image-Based Real-Time Fire Detection using Deep Learning with Data Augmentation for Vision-Based Surveillance Applications

Kang, L., Wang, I.-S., Chou, K.-L., Chen, S.-Y., & Chang, C. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909899 · Cited by 30 · see citing works

A Novel Forest Fire Detection System Using Fuzzy Entropy Optimized Thresholding and STN-based CNN

Avula, S. B., Badri, S., & Gokul Reddy, P. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027347 · Cited by 29 · see citing works

Nearest neighbors and density-based undersampling for imbalanced data classification with class overlap

Sun, P., Du, Y., & Xiong, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128492 · Cited by 25 · see citing works

Early indoor occluded fire detection based on firelight reflection characteristics

Xie, Y., Zhu, J., Guo, Y., You, J., Feng, D., & Cao, Y. (2022) · Fire Safety Journal · doi:10.1016/j.firesaf.2022.103542 · Cited by 25 · see citing works

Optimized cross-module attention network and medium-scale dataset for effective fire detection

Khan, Z. A., Ullah, F. U. M., Yar, H., Ullah, W., Khan, N., Kim, M. J., & Baik, S. W. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111273 · Cited by 24 · see citing works

A density-based oversampling approach for class imbalance and data overlap

Zhang, R., Lu, S., Yan, B., Yu, P., & Tang, X. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109747 · Cited by 24 · see citing works

SA-CGAN: An oversampling method based on single attribute guided conditional GAN for multi-class imbalanced learning

Dong, Y., Xiao, H., & Dong, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.135 · Cited by 23 · see citing works

Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment

Nadeem, M., Dilshad, N., Alghamdi, N. S., Dang, L. M., Song, H., Nam, J., & Moon, H. (2023) · Smart Cities · doi:10.3390/smartcities6050103 · Cited by 22 · see citing works

YOLO-EPF: Multi-scale smoke detection with enhanced pool former and multiple receptive fields

Yin, D., Cheng, P., & Huang, Y. J. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104511 · Cited by 20 · see citing works

Fire detection in video surveillance using superpixel-based region proposal and ESE-ShuffleNet

Wang, P., Zhang, J., & Zhu, H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11261-9 · Cited by 19 · see citing works

A study of engine room smoke detection based on proactive machine vision model for intelligent ship

Zhang, P., Song, Z., Li, C., Liu, Y., Zou, Y., Zhang, Y., & Sun, P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122689 · Cited by 17 · see citing works

HelpResponder—System for the Security of First Responder Interventions

Rodriguez‐Sanchez, M. C., Fernández-Jiménez, L., Jiménez, A. R., Vaquero, J., Borromeo, S., & Galilea, J. L. L. (2021) · Sensors · doi:10.3390/s21082614 · Cited by 16 · see citing works

Influence of Compartment Fire Behavior at Ignition and Combustion Development Stages on the Operation of Fire Detectors

Zhdanova, A. O., Волков, Р. С., Sviridenko, A. S., Кузнецов, Г. В., & Strizhak, P. A. (2022) · Fire · doi:10.3390/fire5030084 · Cited by 16 · see citing works

Citizen empowerment through smart surveillance: evidence from Indian smart cities

Khanna, P., & Khanra, S. (2023) · Digital Policy Regulation and Governance · doi:10.1108/dprg-11-2022-0141 · Cited by 16 · see citing works

Real-Time Early Indoor Fire Detection and Localization on Embedded Platforms with Fully Convolutional One-Stage Object Detection

Li, Y., Shang, J., Meng, Y., Ding, B., & Zhong, J. (2023) · Sustainability · doi:10.3390/su15031794 · Cited by 15 · see citing works

Saliency Based Fire Detection Using Texture and Color Features

Jamali, M., Karimi, N., & Samavi, S. (2020) · Iranian Conference on Electrical Engineering · doi:10.1109/icee50131.2020.9260659 · Cited by 15 · see citing works

A lightweight smoke detection network incorporated with the edge cue

Wang, J., Zhang, X., & Zhang, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122583 · Cited by 14 · see citing works

Complementing IIoT Services Through AI: Feasibility and Suitability

Banaie, F., & Hashemzadeh, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-76613-9_2 · Cited by 14 · see citing works

A geometric-based clustering method using natural neighbors

Pourbahrami, S., & Hashemzadeh, M. (2022) · Information Sciences · doi:10.1016/j.ins.2022.08.047 · Cited by 14 · see citing works

An efficient fire detection algorithm based on multi‐scale convolutional neural network

Cheng, Y., Chen, K., Bai, H., Mou, C., Zhang, Y., Yang, K., Gao, Y., & Liu, Y. (2021) · Fire and Materials · doi:10.1002/fam.3045 · Cited by 13 · see citing works

High-Precision Real-Time Forest Fire Video Detection Using One-Class Model

Yang, X., Wang, Y., Liu, X., & Liu, Y. (2022) · Forests · doi:10.3390/f13111826 · Cited by 12 · see citing works

Wireless Sensor Networks using image processing for fire detection

Ateeq, Z., & Momani, M. (2020) · International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) · doi:10.1109/citisia50690.2020.9371798 · Cited by 12 · see citing works

FADA-SMOTE-Ms: Fuzzy Adaptative Smote-Based Methods

Roudani, M., & Moutaouakil, K. E. (2024) · IEEE Access · doi:10.1109/access.2024.3480848 · Cited by 11 · see citing works

A triple interference removal network based on temporal and spatial attention interaction for forest smoke recognition in videos

Tao, H. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108756 · Cited by 11 · see citing works

The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Ghufron, G., Surarso, B., & Gernowo, R. (2020) · Jurnal Ilmiah KURSOR · doi:10.21107/kursor.v10i3.232 · Cited by 10 · see citing works

An efficient deep neural network with color-weighted loss for fire detection

Zhang, R., Zhang, W., Liu, Y., Li, P., & Zhao, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12861-9 · Cited by 10 · see citing works

Research on Improved YOLOv3 Fire Detection Based on Enlarged Feature Map Resolution and Cluster Analysis

Yue, C., & Ye, J. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1757/1/012094 · Cited by 9 · see citing works

Real-time semi-occluded fire detection and evacuation route generation: Leveraging instance segmentation for damage estimation

Shirwaikar, R. D., Narvekar, A., Hosamani, A., Fernandes, K., Tak, K., & Parab, V. (2025) · Fire Safety Journal · doi:10.1016/j.firesaf.2025.104338 · Cited by 8 · see citing works

Mobile Terminal Video Image Fuzzy Feature Extraction Simulation Based on SURF Virtual Reality Technology

Feng, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3019070 · Cited by 8 · see citing works

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi, D., AntoBennet, M., Renjith, P. N., Arun, M. R., & Vanathi, B. (2023) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.110217 · Cited by 8 · see citing works

A dual-stage deep learning framework for simultaneous fire and firearm detection in smart surveillance systems

Pravesh, R., & Sahana, B. C. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106330 · Cited by 6 · see citing works

Efficient Fire Detection with E-EFNet: A Lightweight Deep Learning-Based Approach for Edge Devices

Farman, H., Nasralla, M. M., Khattak, S. B. A., & Jan, B. (2023) · Applied Sciences · doi:10.3390/app132312941 · Cited by 6 · see citing works

Vision-based detection algorithm for monitoring dynamic change of fire progression

Suh, Y. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01211-9 · Cited by 6 · see citing works

Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

de Domingo, M., Ortigosa, N., Sevilla, J., & Roger, S. (2021) · Sensors · doi:10.3390/s21030797 · Cited by 6 · see citing works

Dynamic strategy to use optimum memory space in real-time video surveillance

Biswas, T., Bhattacharya, D., & Mandal, G. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04521-z · Cited by 6 · see citing works

Image Processing-Based Fire Detection and Protection System Using OPENCV

Verma, V. K., Jain, K., Kumari, J., Kumar, R., & Kumar, U. (2023) · International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET) · doi:10.1109/icseiet58677.2023.10303598 · Cited by 6 · see citing works

A Novel Segmentation Method for Furnace Flame Using Adaptive Color Model and Hybrid‐Coded HLO

Zhang, P., Fei, M., Wang, L., Wu, X., Peng, C., & Chen, K. (2021) · Complexity · doi:10.1155/2021/3027126 · Cited by 6 · see citing works

YOLOFlame: Benchmarking vehicle fire and smoke detection in urban environments

Dilshad, N., Parez, S., Khan, S. U., Khan, M., Mahmood, T., Alghamdi, N. S., & Song, J. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.03.008 · Cited by 5 · see citing works

Deep Learning-based Wildfire Smoke Detection using Uncrewed Aircraft System Imagery

Mahmud, K. R., Wang, L., Liu, X., Jiahao, L., & Hassan, S. (2024) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur61395.2024.10597449 · Cited by 5 · see citing works

Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model

Daoudi, R., Mouelhi, A., Bouchouicha, M., Moreau, É., & Sayadi, M. (2023) · Electronics · doi:10.3390/electronics12183888 · Cited by 5 · see citing works

Machine Learning based Classification for Fire and Smoke Images Recognition

Jabnouni, H., Arfaoui, I., Cherni, M. A., Bouchouicha, M., & Sayadi, M. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9803928 · Cited by 5 · see citing works

ACQC: Apollonius Circle‐based Quantum Clustering

Abdolmaleki, N., Khanli, L. M., Hashemzadeh, M., & Pourbahrami, S. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101877 · Cited by 5 · see citing works

SSmokeDet: A novel network dedicated to small-scale smoke detection

Wang, J., Wang, L., Zhang, R., Li, X., & Fan, B. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110092 · Cited by 4 · see citing works

O-LGMD: An Opponent Colour LGMD-Based Model for Collision Detection with Thermal Images at Night

Zhang, Y., Zhao, J., Hua, M., Luan, H., Liu, M., Fang, L., Cuayáhuitl, H., & Yue, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15934-3_21 · Cited by 4 · see citing works

Deep Convolutional Neural Networks based on Manifold for Smoke Recognition

Cheng, M., Ma, P., He, R., Chen, J., Zhang, Z., & Huang, J. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534297 · Cited by 4 · see citing works

Penerapan Algoritma K-Medoids Pada Clustering Penerima Bantuan Pangan Non Tunai (BPNT)

Ramayanti, T., Haerani, E., Jasril, J., & Oktavia, L. (2023) · JURNAL MEDIA INFORMATIKA BUDIDARMA · doi:10.30865/mib.v7i3.6475 · Cited by 4 · see citing works

Improved Yolov4-tiny for Fire Detection

Li, Q., Xiang, Q., & Xie, Y. T. (2024) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd64080.2024.10702272 · Cited by 4 · see citing works

Establish intelligent detection system to evaluate the sugar smoking of chicken thighs

Wang, B., Yang, H., Lu, F., Yu, F., Wang, X., Zou, Y., Liu, D., Zhang, J., & Xia, W. (2021) · Poultry Science · doi:10.1016/j.psj.2021.101447 · Cited by 4 · see citing works

FireMamba: An efficient visual representation learning framework for fire detection

Khan, S. U., Danish, S., Iqbal, E., Gupta, D., Zweiri, Y., & Abdulrahman, Y. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.10.001 · Cited by 3 · see citing works

A new strategy for fire detection based on analyzing the irregularity pattern of image contour

Oghabi, E., Moghaddam, R. K., & Kobravi, H.-R. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03303-0 · Cited by 3 · see citing works

The Multi-Scale Depth-Separable Convolution Network for Fire and Smoke Detection

Yan, H., Cui, Z., Zhao, H., Zhang, J., Qin, J., & Guo, Q. (2024) · Combustion Science and Technology · doi:10.1080/00102202.2024.2372689 · Cited by 3 · see citing works

Использование видеоаналитики для раннего обнаружения возгорания

Glotov, M., Кропотова, С. С., & Strizhak, P. A. (2025) · Pozharovzryvobezopasnost/Fire and Explosion Safety · doi:10.22227/0869-7493.2025.34.01.70-78 · Cited by 2 · see citing works

Analysis of Deep Learning Techniques Used for Indoor Flame Detection

Kundu, T., & Senapati, K. K. (2022) · International Conference on IoT and Blockchain Technology (ICIBT) · doi:10.1109/icibt52874.2022.9807802 · Cited by 2 · see citing works

Fire Detection on Video Using ViBe Algorithm and LBP-TOP

Ramadhani, K. N., Sthevanie, F., Kosala, G., & Putra, K. S. (2022) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v6i6.4164 · Cited by 2 · see citing works

A hierarchical Transformer network for smoke video recognition

Cheng, G., Xian, B., Liu, Y., Chen, X., Hu, L., & Song, Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104959 · Cited by 2 · see citing works

Real-Time Fire Detection Model Based on Attention Mechanism and Multi-scale Features Map

Yang, J., Liu, Y., Fang, W., Jia, G., & Qiu, Y. (2022) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc57393.2022.9995156 · Cited by 2 · see citing works

Medical Image Segmentation using Enhanced Feature Weight Learning Based FCM Clustering

Koundal, A., Budhiraja, S., & Agrawal, S. (2024) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/3056 · Cited by 1 · see citing works

Algorithm for identifying the location of a potential indoor fire source, its characteristics, and the cause of fire

Volkov, R. S., Kuznetsov, G. V., Strizhak, P. A., & Кропотова, С. С. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.108455 · Cited by 1 · see citing works

FW-S3PFCM: feature-weighted safe-semi-supervised possibilistic fuzzy C-means clustering

Khezri, S., Aghazadeh, N., Hashemzadeh, M., & Oskouei, A. G. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01607-6 · Cited by 1 · see citing works

Comprehensive Survey on Fire Detection with Machine Learning and Deep Learning Models

Shankar, G., & Geetha, M. K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-3035-5_30 · Cited by 1 · see citing works

Outlier Detection on Data Streams Using a QLattice-based Model and Online Learning

Fardin, S., & Hashemzadeh, M. (2023) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.20.2.81 · Cited by 1 · see citing works

A fast flame recognition algorithm based on tracking and marking

Li, J., Bu, W., & Chen, J. W. (2020) · Chinese Automation Congress · doi:10.1109/cac51589.2020.9326932 · Cited by 1 · see citing works

Application of Database Technology in Video Surveillance System

Zhong, C. (2022) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs56523.2022.10060827 · Cited by 1 · see citing works

Fire detection system on surveillance videos using faster region-based convolutional neural network for high buildings evacuation

Lestari, D. P., Kosasih, R., Sari, I., & Amalia, A. (2023) · AIP conference proceedings · doi:10.1063/5.0116307 · Cited by 1 · see citing works

Photovoltaic Systems for the Supply of Video Surveillance Equipment in Urban Environment

Favaron, G., & Benedito, R. D. S. (2019) · IEEE Latin America Transactions · doi:10.1109/tla.2019.8986425

Continuous Wavelet Transform Based Gene Optimized Fuzzy C-Means Clustering For Forest Fire Detection

(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.j1002.08810s19

Vision Based Flame Detection Using Compressed Domain Motion Prediction and Multi-Feature Fusion

Tang, J., Chen, J., Wang, X., Pu, B., & Liu, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6483-3_34

Multichannel Monitoring of the Effectiveness of Fire Extinguishing in a Roommultichannel Monitoring of the Effectiveness of Fire Extinguishing in a RoomPreprint

Zhdanova, A. O., Egorov, R. I., & Strizhak, P. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5137210

Fire Detection Based on Computer Vision Using OpenCV

Reddy, K. D. P. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem47258

Development of an Autonomous Firefighting Quadruped Robot With a Novel Vision-Based Fire Segmentation System

Baird, C., & Nokleby, S. (2025) · ASME Letters in Translational Robotics · doi:10.1115/1.4070688

Enhancing Surveillance Systems Transforming Cameras into Intelligent CCTV Systems with CSS: An Experimental Study

Kesavan, R., Kesavan, S., Geetha, R. N., Vidhya, S., Mukunthan, M. A., & Anand, A. J. (2026) · Premier journal of science. · doi:10.70389/pjs.100208

A convolution-augmented swin transformer for robust fire detection with enhanced anti-interference capability

Xie, K., He, H., Zhu, J., Huang, D., Zhang, Q., & You, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115429

FireJet: A coupled visual perception system for water-jet guidance in industrial firefighting robots

Wang, M., Zuo, Q., Zheng, H., & Huang, X. (2026) · Journal of Loss Prevention in the Process Industries · doi:10.1016/j.jlp.2026.106148

Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Daoud, Z., Hamida, A. B., & Amar, C. B. (2019) · Journal of WSCG · doi:10.24132/jwscg.2019.27.1.4 · Cited by 3 · see citing works

Video-based fire detection with spatio-temporal SURF and color features

Shi, L., Long, F., Zhan, Y., & Lin, C.-H. (2016) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2016.7578269 · Cited by 6 · see citing works

VIDEO BASED FLAME DETECTION - Using Spatio-temporal Features and SVM Classification

Dimitropoulos, K., Tsalakanidou, F., & Grammalidis, N. (2012) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0003858104530456 · Cited by 2 · see citing works

Spectral Spatio-Temporal Fire Model for Video Fire Detection

Wu, Z., Song, T., Wu, X., Shao, X., & Liu, Y. (2017) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001418500131 · Cited by 10 · see citing works

Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Dimitropoulos, K., Barmpoutis, P., & Grammalidis, N. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2339592 · Cited by 264 · see citing works