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Early fire detection using convolutional neural networks during surveillance for effective disaster management
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“For instance, the outcomes of the Mivia dataset experiment achieved an accuracy of 95.32%, outperforming another CNN-based system [132] that achieved 94.39% accuracy.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [132]
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Items 201–300 of 558
Sharma, H., & Kanwal, N. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126655 · Cited by 18
Yuvaraj, N., Karthikeyan, T., Praghash, K., & Reddy, K. H. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08883-5 · Cited by 16
ResNet-50 based fire and smoke images classification
Jabnouni, H., Arfaoui, I., Cherni, M. A., Bouchouicha, M., & Sayadi, M. (2022) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip55956.2022.9805875 · Cited by 16
Fire identification based on improved multi feature fusion of YCbCr and regional growth
Chen, X., An, Q., & Yu, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122661 · Cited by 16
Madafri, I. E., Peña, M., & Torre, N. O. (2024) · Mathematics · doi:10.3390/math12040534 · Cited by 18
Jiang, Y. Y., Chen, H., Zhang, X., Zhou, Y., & Wang, L. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3118135 · Cited by 16
“Therefore, flame image-based modeling methods have been widely applied to detect combustion ON/OFF [4], clinker quality [5], sintering [6], [7], and stability conditions [8], [9] in furnaces, such as power plant boilers [10], biomass boilers [11], gas turbines [12], etc.”
MethodDG-YOLO: A Novel Efficient Early Fire Detection Algorithm Under Complex Scenarios
Jiang, X., Xu, L., & Fang, X. (2024) · Fire Technology · doi:10.1007/s10694-024-01672-z · Cited by 15
Devika, G., & Karegowda, A. G. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-2108-3.ch001 · Cited by 15
Classification and pattern extraction of incidents: a deep learning-based approach
Sarkar, S., Vinay, S., Djeddi, C., & Maiti, J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06780-3 · Cited by 15
“application areas, including fraud detection [40], dynamic planning of public bicycle-sharing system [46], time series prediction [47], Spark-based computation [48, 49], pattern recognition [50], speech recognition [51], classification [52, 53], image processing [54, 55], and video processing [41].”
BackgroundMultiscale fire image detection method based on CNN and Transformer
Wu, S., Sheng, B., Fu, G., Zhang, D., & Jian, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17482-4 · Cited by 15
“Muhammad proposed a CNN-based method for early fire detection [13].”
InfluentialFire/Flame Detection with Attention-Based Deep Semantic Segmentation
Aliser, A., & Duranay, Z. B. (2024) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-024-00697-y · Cited by 15
“In Muhammad et al. (2018), a sophisticated early fire detection framework employing convolutional neural networks (CNNs) was presented, specifically designed for closed-circuit television (CCTV) security cameras.”
An Explainable AI-Based Modified YOLOv8 Model for Efficient Fire Detection
Hasan, M. W., Shanto, S., Nayeema, J., Rahman, R., Helaly, T., Rahman, Z., & Mehedi, S. T. (2024) · Mathematics · doi:10.3390/math12193042 · Cited by 16
FireNet: A Lightweight and Efficient Multi-Scenario Fire Object Detector
He, Y., Sahma, A., He, X., Wu, R., & Zhang, R. (2024) · Remote Sensing · doi:10.3390/rs16214112 · Cited by 15
Faster R-CNN Based Indoor Flame Detection for Firefighting Robot
Guo, J., Hou, Z., Xie, X., Yao, S., Wang, Q., & Jin, X. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002843 · Cited by 15
“obstacle detection, visualization of the human body for diagnosis [2], [3], [4], ship monitoring and surveillance [5].”
BackgroundMulti‐step implicit Adams predictor‐corrector network for fire detection
Deng, Z., Hu, S., Yin, S., Wang, Y., Basu, A., & Cheng, I. (2022) · IET Image Processing · doi:10.1049/ipr2.12491 · Cited by 15
Space‐Air‐Ground Integrated Network for Disaster Management: Systematic Literature Review
Anjum, M. J., Anees, T., Tariq, F., Shaheen, M., Amjad, S., Iftikhar, F., & Ahmad, F. (2023) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2023/6037882 · Cited by 15
FireClassNet: a deep convolutional neural network approach for PJF fire images classification
Daoud, Z., Hamida, A. B., & Amar, C. B. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08750-3 · Cited by 14
“As an example, Dunnings et al. fine-tuned in their work [24] the AlexNet for a simple binary classification.”
BackgroundInfluentialA Flame-Detection Algorithm Using the Improved YOLOv5
Xie, X., Chen, K., Guo, Y., Tan, B., Chen, L., & Huang, M. (2023) · Fire · doi:10.3390/fire6080313 · Cited by 14
Flame and Smoke Semantic Dataset: Indoor Fire Detection with Deep Semantic Segmentation Model
Hou, F., Rui, X., Chen, Y., & Fan, X. (2023) · Electronics · doi:10.3390/electronics12183778 · Cited by 14
An outdoor fire recognition algorithm for small unbalanced samples
Song, X., Gao, S., Liu, X., & Chen, C. (2021) · Alexandria Engineering Journal · doi:10.1016/j.aej.2021.01.028 · Cited by 15
Kalaivani, V., & Chanthiya, P. (2022) · Earth Science Informatics · doi:10.1007/s12145-022-00801-y · Cited by 14
Meter Digit Recognition Via Faster R-CNN
Waqar, M., Waris, M. A., Rashid, E., Nida, N., Nawaz, S., & Yousaf, M. H. (2019) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai47710.2019.8967357 · Cited by 14
“Recent success in deep neural networks has remarkably improved various computer vision tasks including surveillance, medicine and surgery, robotics, self-driving cars and many other areas [1], [2], [3], [4], [5], [6], [7].”
BackgroundAn 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
“However, some missed detection problems still exist for small and weak fire targets (videos 13 and 14).”
BackgroundMethodLi, Y., Shang, J., Meng, Y., Ding, B., & Zhong, J. (2023) · Sustainability · doi:10.3390/su15031794 · Cited by 15
Fire detection using vision transformer on power plant
Zhang, K., Wang, B., Tong, X., & Liu, K. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.05.224 · Cited by 13
Dwivedi, U. K., Wiwatcharakoses, C., & Sekimoto, Y. (2022) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme55909.2022.9987855 · Cited by 13
“[27] proposed an early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance camera that reaches an accuracy of 94.”
MethodVideo Based Smoke and Flame Detection Using Convolutional Neural Network
Son, G.-Y., Park, J., Yoon, B., & Song, J.-G. (2018) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2018.00063 · Cited by 13
“Another method is by using videos obtained from the existing CCTV cameras as the input for the fire detection system to reduce the expense for such system [10].”
MethodYan, Z., Wang, L., Qin, K., Zhou, F., Ouyang, J., Wang, T., Hou, X., & Bu, L. (2022) · Forests · doi:10.3390/f14010052 · Cited by 12
Phoenix: Aerial Monitoring for Fighting Wildfires
Çoğay, S., & Seçinti, G. (2022) · Drones · doi:10.3390/drones7010019 · Cited by 12
Latif, A., & Chung, H. (2023) · Fire Technology · doi:10.1007/s10694-023-01426-3 · Cited by 12
“[1] used AlexNet as a baseline architecture and modified it for the purpose of fire detection.”
BackgroundMethodDeep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information
Anggraeni, S. R., Ranggianto, N. A., Ghozali, I., Fatichah, C., & Purwitasari, D. (2022) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.8.1.31-41 · Cited by 12
“[4], examining early fire detection using a fine-tuned convolutional neural network (CNN) on a CCTV camera.”
BackgroundHigh-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
Deep Learning Assisted Medical Insurance Data Analytics With Multimedia System.
Zhang, C., Vinodhini, B., & Muthu, B. (2023) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2023.01.009 · Cited by 12
Real-Time Forest Fire Detection Using IoT and Smart Sensors
Nagolu, C., Cheekula, C., Thota, D. S. K., Padmanaban, K., & Bhattacharyya, D. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134063 · Cited by 12
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
“Recently, researchers try to use CNN based methods in fire detection [23-25].”
BackgroundEFNet-CSM: EfficientNet with a modified attention mechanism for effective fire detection
Yar, H., Ullah, F. U. M., Khan, Z. A., Kim, M. J., & Baik, S. W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114353 · Cited by 11
Artificial intelligence in disaster management: achievements, challenges, and prospects
Xu, J., Nair, D. J., & Waller, S. T. (2025) · Natural Hazards · doi:10.1007/s11069-025-07667-5 · Cited by 14
Wu, S., Di, B., Ustin, S. L., Wong, M. S., Adhikari, B. R., Zhang, R., & Luo, M. (2023) · Remote Sensing · doi:10.3390/rs15020299 · Cited by 11
Malebary, S. J. (2023) · Sensors · doi:10.3390/s23229043 · Cited by 14
“Another CNN-based fire detection technique [28] was proposed to implement smart surveillance, which was trained on two-level datasets.”
MethodWildfire Smoke Detection System: Model Architecture, Training Mechanism, and Dataset
Wang, C., Xu, C., Akram, A., Wang, Z., Shan, Z., & Zhang, Q. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/1610145 · Cited by 11
“For example, References [28–30] use CNN to extract deep features instead of manual features for image smoke detection, and [28] emphasize the use of batch normalization (BN) in the network.”
Smart city and fire detection using thermal imaging
Mazur-Milecka, M., Głowacka, N., Kaczmarek, M., Bujnowski, A., Kaszynski, M., & Rumiński, J. (2021) · Conference on Human System Interactions · doi:10.1109/hsi52170.2021.9538699 · Cited by 11
Accelerated Fire Detection and Localization at Edge
Mukherjee, A., Mondal, J., & Dey, S. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3510027 · Cited by 11
“[41] ran a small custom CNN model for early fire detection at 17 FPS in Core i5 system with 8 GB RAM.”
MethodWang, M., Jiang, L., Yue, P., Yu, D., & Tuo, T. (2022) · doi:10.5194/essd-2022-394 · Cited by 11
Industrial and Mining Fire Detection Algorithm Based on Improved YOLO
Li, X., Fan, Y., Liu, Y., Li, X., & Liu, Z. (2024) · Fire Technology · doi:10.1007/s10694-024-01635-4 · Cited by 10
Yuan, J., Wang, H., Yang, T., Su, Y., Song, W., Li, S., & Gong, W. (2024) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2024.105515 · Cited by 10
Forest fire detection in aerial vehicle videos using a deep ensemble neural network model
Basturk, N. S. (2023) · Aircraft Engineering and Aerospace Technology · doi:10.1108/aeat-01-2022-0004 · Cited by 11
Lightweight Convolutional Neural Network for Fire Classification in Surveillance System
Nguyen, D.-L., Putro, M. D., & Jo, K.-H. (2023) · IEEE Access · doi:10.1109/access.2023.3305455 · Cited by 10
Channel‐wise attention model‐based fire and rating level detection in video
Wu, Y., He, Y., Shivakumara, P., Li, Z., Guo, H., & Lü, T. (2019) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2019.0022 · Cited by 10
“The method [22] proposes a convolution neural network for early fire detection during disaster management.”
MethodFacilitating mmWave Mesh Reliability in PPDR Scenarios Utilizing Artificial Intelligence
Pirmagomedov, R., Moltchanov, D., Ometov, A., Muhammad, K., Andreev, S. D., & Koucheryavy, Y. A. (2019) · IEEE Access · doi:10.1109/access.2019.2958426 · Cited by 10
“and outdoor environments, where 14 videos contain fire and 17 videos belong to the non-fire class [70].”
BackgroundMethodVisual attention-based comparative study on disaster detection from social media images
Arif, A., Amin, M. A., Ali, A. A., & Rahman, A. K. M. M. (2020) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-020-00368-1 · Cited by 10
“CNNs have shown state-of-the-art efficiency in a multitude of computer vision challenges, including image classification and retrieval, object detection, and segmentation of images [14].”
BackgroundEarly Fire Detection Using Deep Learning
Jha, A., Vedak, S., Mundada, K., Walnuskar, R., Chopade, U., & Iyer, A. (2021) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv53313.2021.9670963 · Cited by 10
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
“The accuracy of the CNN-based methods has been significantly improved [8, 21, 29, 31–34 , Although the performance of the CNN-based methods has been greatly improved than traditional methods, there are still some problems.”
BackgroundMethodKong, L., Li, J., Guo, S., Zhou, X., & Wu, D. (2024) · Journal of Civil Engineering and Management · doi:10.3846/jcem.2024.21360 · Cited by 9
A deep learning based approach for semantic segmentation of small fires from UAV imagery
Saxena, V., Jain, Y., & Mittal, S. (2025) · Remote Sensing Letters · doi:10.1080/2150704x.2024.2449475 · Cited by 9
“Muhammad, Ahmad, and Baik (2018) detect fire using fine-tuned CNNs.”
Sajid, M., Khan, A. H., Malik, K. R., Khan, J. A., & Alwadain, A. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2944 · Cited by 9
Autonomous navigation of non-perception firefighting robot through CCTV-informed vision sharing
Deng, R., Ding, S., Ding, Y., Wang, M., Huang, X., & Usmani, A. S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129210 · Cited by 9
Papakis, I., Linardos, V., & Drakaki, M. (2025) · Remote Sensing · doi:10.3390/rs17193310 · Cited by 9
“Recent advancements in machine learning (ML) and deep learning (DL) have facilitated the development of data-driven approaches for disaster management [5], including wildfire risk assessment, detection, and prediction [5–22].”
InfluentialMultiple Instance Learning for CNN Based Fire Detection and Localization
Aktaş, M., Bayramcavus, A., & Akgün, T. (2019) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2019.8909842 · Cited by 10
“Motivated by the state-of-the-art performance achieved by convolutional neural networks (CNN) in visual detection and classification tasks, CNNs have recently been applied to the visual fire detection problem [1, 2, 3].”
MethodMaguraushe, K., Ndayizigamiye, P., & Bokaba, T. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50192-0_9 · Cited by 9
Indoor and Outdoor Fire Localization Using YOLO Algorithm
Nori, R. R., Farhan, R. N., & Abed, S. H. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2114/1/012067 · Cited by 9
“Third further analysis for the focus region[2].”
MethodUnmanned Engine Room Surveillance Using an Autonomous Mobile Robot
Kim, S.-D., & Bae, C. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11030634 · Cited by 9
Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
Li, C., Zhu, B., Chen, G., Li, Q., & Xu, Z. (2025) · Applied Sciences · doi:10.3390/app15042127 · Cited by 8
Video Fire Recognition Using Zero-Shot Vision-Language Models Guided by a Task-Aware Object Detector
Gragnaniello, D., Greco, A., Sansone, C., & Vento, B. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3721291 · Cited by 8
Khosravi, K., Mosallanejad, A., Bateni, S. M., Kim, D., Jun, C., Shahvaran, A. R., Farooque, A. A., Karbasi, M., & Ali, M. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2025.179369 · Cited by 9
Zhang, K., Mao, B., Liu, H., & Zhang, Y. (2025) · Measurement · doi:10.1016/j.measurement.2025.117948 · Cited by 8
Gopal, L. S., Prabha, R., Thirugnanam, H., Ramesh, M. V., & Malamud, B. D. (2026) · Natural hazards and earth system sciences · doi:10.5194/nhess-26-215-2026 · Cited by 12
Nicolas-Mindoro, J. G. (2019) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2019/21862019 · Cited by 8
Different Approaches to Background Subtraction and Object Tracking in Video Streams: A Review
Sivanantham, K., Blessington Praveen, P., & Mohan Kumar, R. (2023) · Contributions to environmental sciences & innovative business technology · doi:10.1007/978-981-99-3288-7_2 · Cited by 8
Early Forest Fire Detection Based on Deep Learning
Li, M., Zhang, Y. M., Mu, L., Xin, J., Yu, Z., Liu, H., & Xie, G. (2021) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iai53119.2021.9619342 · Cited by 8
Neural fuzzy system design in forest fire detection
Rao, G. N., Rao, P. J., Duvvuru, R., Beulah, K., Lydia, E. L., Rathnala, P., Balakrishna, B., & Motru, V. R. (2023) · Microsystem Technologies · doi:10.1007/s00542-023-05496-9 · Cited by 8
Multi-layer capsule network with joint dynamic routing for fire recognition
Wu, Y., Cen, L., Kan, S., & Xie, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104825 · Cited by 9
Tongue print identification using deep CNN for forensic analysis
Sadasivan, S., Sivakumar, T. T., Joseph, A. P., Zacharias, G. C., & Nair, P. (. ). M. S. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179722 · Cited by 8
“Deep CNN architecture uses stack of layers to learn features where the initial layers extracts local features like blobs and edges and the final layers are capable of extracting global features that are used for the recognition [13].”
Zhao, J., Li, W., Zhu, J., Gao, Z. P., Pan, L., & Liu, Z. (2024) · Journal of Field Robotics · doi:10.1002/rob.22438 · Cited by 7
“Due to the frequent occurrence of fire accidents and the difficulties in responding to them, significant casualties and property losses have been caused (Madridano et al. 2021; Muhammad, Ahmad, and Baik 2018).”
Li, C., Wu, D., Zhang, X., & Wu, P. (2025) · IEEE Access · doi:10.1109/access.2025.3560393 · Cited by 7
“To mitigate such disasters, it is crucial to detect fires in their early stages while avoiding false alarms [4].”
High-performance fire detection framework based on feature enhancement and multimodal fusion
Zhou, Z., Zhao, H., Li, X., Liu, Y., Jiang, T., Jing, J., & Guo, Y. (2025) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2025.03.004 · Cited by 8
Real-time aerial fire detection on resource-constrained devices using knowledge distillation
Jangirova, S., Jankovic, B., Ullah, W., Khan, L. U., & Guizani, M. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104665 · Cited by 7
Reliable Indoor Fire Detection Using Attention-Based 3D CNNs: A Fire Safety Engineering Perspective
Ali, M. M. E. H., & Ghodrat, M. (2025) · Fire · doi:10.3390/fire8070285 · Cited by 7
A hybrid attention model based on first-order statistical features for smoke recognition
Guo, N., Liu, J., Di, K., Gu, K., & Qiao, J. (2024) · Science China Technological Sciences · doi:10.1007/s11431-022-2449-1 · Cited by 7
An efficient fire detection algorithm based on Mamba space state linear attention
Li, Y., Wang, Y., Shao, X., & Zheng, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-95162-z · Cited by 7
Wang, K., Qiu, R., Chen, H., Liu, X., Wang, H., & Liu, J. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.127467 · Cited by 7
Yang, F., Cai, Z., Su, L., Xue, Y., Shen, Y., & Wang, J. (2022) · International Journal of Metrology and Quality Engineering · doi:10.1051/ijmqe/2022001 · Cited by 7
Zhang, L., Zhu, Y., Wu, H., & Li, K. (2022) · IEEE Access · doi:10.1109/access.2022.3170464 · Cited by 7
“[16] constructed an early fire detection framework by adding depth features of convolutional neural network (CNN); Saeed et al .”
MethodDistributed Fire Detection and Localization Model Using Federated Learning
Hu, Y., Fu, X., & Zeng, W. (2023) · Mathematics · doi:10.3390/math11071647 · Cited by 7
“In 2018, Muham-mad et al. [6] studied four different fire detection models using convolutional neural networks for early fire detection.”
MethodDetecting Fire in Color Images using Convolutional Neural Network Architectures
Tanmayi, D., Udaya, E., & Pati, P. B. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126490 · Cited by 7
“The environmental sensors, typically employed in indoor environments, detect fire only in a close-range Muhammed et al. [5].”
BackgroundMethodSmart Surveillance System using Deep Learning and RaspberryPi
Patel, K., & Patel, M. R. (2021) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc51209.2021.9528194 · Cited by 7
Pham, H. V., & Moore, P. T. (2019) · Applied Sciences · doi:10.3390/app9183910 · Cited by 7
Smoke Detection on the Edge: A Comparative Study of YOLO Algorithm Variants
Polenakis, I., Sarantidis, C., Karydis, I., & Avlonitis, M. (2025) · Signals · doi:10.3390/signals6040060 · Cited by 6
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
EFFICIENT DEEP CNN-BASED FIRE DETECTION AND LOCALIZATION IN VIDEO SURVEILLANCE APPLICATIONS
(2022) · International Journal For Innovative Engineering and Management Research · doi:10.48047/ijiemr/v11/i06/43 · Cited by 6
Robust stacking-based ensemble learning model for forest fire detection
Akyol, K. (2023) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-023-05194-z · Cited by 6
Design of visual communication based on deep learning approaches
Lu, L. (2019) · Soft Computing · doi:10.1007/s00500-019-03954-z · Cited by 6
Sridhar, P., & Sathiya, R. R. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1916/1/012027 · Cited by 6
“[10] Muhammad, Khan, Jamil Ahmad, and Sung WookBaik.”
RetractedMartinka, J., Nečas, A., & Rantuch, P. (2021) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-021-10903-2 · Cited by 6
Design of an Intelligent Alarm System Based on Multi-sensor Data Fusion
Wen, C., Li, K., Liao, Y., & Xiao, Z. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1961/1/012025 · Cited by 6
“Khan Muhammad and Jamil Ahmad propose an early fire detection framework using fine-tuned convolutional neural networks[8].”
BackgroundAli, M. M. E. H., & Ghodrat, M. (2025) · IEEE Access · doi:10.1109/access.2025.3598919 · Cited by 5
“Similarly, [41] and [55] relied on color-based or object-specific cues from constrained datasets.”
Advancements in Fire Alarm Detection using Computer Vision and Machine Learning: A Literature Review
Ridhani, M. F., & Mahmudy, W. F. (2023) · Journal of Information Technology and Computer Science · doi:10.25126/jitecs.202382554 · Cited by 5
“This is also done by Muhammad et al who used visual sensors in their research with two environments, namely indoor and outdoor [61].”
MethodMedewar, A. G., Sawarkar, A. D., & Kshirsagar, U. V. (2024) · Cureus Journal of Computer Science. · doi:10.7759/10 · Cited by 5
YOLO-MFD: Object Detection for Multi-Scenario Fires
Mo, F., Liu, S., Wu, S., Chen, R., & Song, T. (2025) · Information · doi:10.3390/info16070620 · Cited by 6
Ryu, J.-K., & Kwak, D.-K. (2022) · Fire · doi:10.3390/fire5060194 · Cited by 5
Wang, S., Han, Y., Yu, M., Wang, H., Wang, Z., Li, G., & Yu, H. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11101945 · Cited by 5
Metadata
- Authors
- Muhammad, K., Ahmad, J., & Baik, S. W.
- Affiliations
- Sejong University (KR)
- Corresponding author
- Baik, Sung Wook
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 288
- Pages
- 30-42
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2780222614
- Cited by
- 558
- References
- 68
- Citations by year
- 2026: 40 · 2025: 62 · 2024: 78 · 2023: 120 · 2022: 87 · 2021: 60 · 2020: 51 · 2019: 43 · 2018: 17
- FWCI
- 44.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- National Research Foundation of Korea (NRF) (2016R1A2B4011712)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire Detection and Safety Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Fire dynamics and safety research
- Keywords
- early fire detection, CCTV surveillance, convolutional neural networks, fine-tuning, disaster management, dynamic channel selection, cognitive radio networks, autonomous response
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