The DOI matches its Crossref record; 66 of the 68 references it cites were found in a registry or an index.
Early fire detection using convolutional neural networks during surveillance for effective disaster management
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
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
A survey on wireless multimedia sensor networks
Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2006) · Computer Networks · doi:10.1016/j.comnet.2006.10.002 · Cited by 2,310
Livescu, K., Bezman, A., Borges, N., Yung, L.-Y., Çetin, Ö., Frankel, J., King, S., Magimai.-Doss, M., Chi, X., & Lavoie, L. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 2,145
Flagged first, then by citations
2 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- D. Guha-Sapir, F. Vos, R. Below, S. Penserre, Annual disaster statistical review 2015: the numbers and trends, 2015. http://www.cred.be/sites/default/files/ADSR_2015.pdf.
Cited by
Sorted by citations
Items 501–558 of 558
Hazardous Chemical Fire Detection Algorithm based on Improved Image Multi-Feature Fusion
Zhang, L. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00072
“In [12]-[15], methods study the scheme that the global challenges arise from forest and urban fires, spurring varied strategies to detect and alleviate early damage.”
BackgroundWang, Z., Wang, Y., Sun, X., & Chenwei, Z. (2023) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3625687.3628391
“Computer vision and image processing for fire detection have limitations due to their dependency on an Line-of-Sight (LOS) path[4].”
A modified Mobilenetv2 architecture for fire detection systems in open areas by deep learning
Mohammed, M. S., Abdullah, N. A. Z., & Abbas, A. H. (2024) · AIP conference proceedings · doi:10.1063/5.0209367
Rapid Damage Detection Using Texture Analysis with Neural Network
Gokilavani, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-61287-9_28
An Effective Real-Time Surveillance System for Fire and Smoke Detection Using CNN
Niranjan, Natesha, B. V., Rashmi, M., & Guddeti, R. M. R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-12700-7_49
CLIP-DS: Incorporating DataStd and Similarity Networks into CLIP for Scene Recognition Task
Zhang, J., Cao, Z., Wu, X., Wu, Y., Zhou, Y., Xiao, Y., Yu, B., & Li, Y. (2024) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai61966.2024.10603215
“The scene recognition task aims to classify images based on their contents and has many applications in the fields of exploration image retrieval [1],[2], robot navigation [3],[4], intelligent video surveillance [5], augmented reality [6],[7], and disaster detection [8].”
BackgroundGopal, L. S. (2024) · doi:10.5194/egusphere-2024-1536-ac1
Gopal, L. S., Prabha, R., Thirugnanam, H., et al. (2024) · doi:10.5194/egusphere-2024-1536-rc2
Segmentasi Wilayah Terdampak Bencana Berdasarkan Fitur Geo-Posisi
Sutedi, A., Julianto, I. T., & Fitriani, L. (2024) · Jurnal Teknologi Informasi dan Ilmu Komputer · doi:10.25126/jtiik.1148557
“…Sepasgozar, 2020), yang dapat dikendalikan secara jarak jauh untuk deteksi banjir (Ogie et al., 2019; Ibrahim and Mishra, 2021), deteksi kebakaran (Muhammad, Ahmad and Baik, 2018), dengan cara menerapkan manajemen bencana melalui satu aplikasi (Muhammad, Ahmad and Baik, 2018; Ogie et al., 2019).”
BackgroundFire Detection System Using Deep CNN
Vikkurty, S., Hegde, P. N., Chinthakrinda, V. P., Hegde, G. P., & SHETTY, S. A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-75605-4_21
Blockchain-based healthcare assistance model in distributed disaster management systems
Sanober, A., & Anwar, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13472-2.00014-0
Fire Detection for Emergency Responders using X
Stefanopoulos, D., Bozas, A., Christodoulou, G., Maslioukova, M. I., Kouloglou, Y., Pegia, M. E., Moumtzidou, A., Gialampoukidis, I., Avgerinakis, K., Vrochidis, S., et al. (2024) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi62980.2024.10858873
Study on early fire prediction based on PCA improved DeepAR model
Yang, H., Yue, Z., Zhao, L., Tong, X., & Zheng, B. (2024) · International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE) · doi:10.1109/icemce64157.2024.10862680
“Consequently, increasing research is being directed towards early fire monitoring technology [1].”
An Efficient Fire Detection Algorithm with Dataset Generation Based on Diffusion
Wang, G., Zheng, H., & Xiao, D. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865462
A Review of Methods and Sensors for Detecting Forest Fire
Gupta, S., Mishra, V. K., Rai, V., & Bishnoi, A. (2024) · International Conference on IoT, Communication and Automation Technology (ICICAT) · doi:10.1109/icicat62666.2024.10923450
Khatri, A., Mahima, M., Kumar, P. K., Ram, B., Dohare, U., & Kumar, S. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01966-1
“Muhummad et al. [23] in the paper introduce an approach to detect fires at an early stage by leveraging convolutional neural networks.”
E-Commerce Logistics Warehouse Fire Monitoring Based on Deep Learning
雷, 乔. (2025) · E-Commerce Letters · doi:10.12677/ecl.2025.1451513
Hybridizing Deep Neural Networks and Machine Learning Models for Natural Scene Image Classification
Kwenda, C., Gwetu, M. V., & Fonou-Dombeu, J. V. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-94439-0_3
34714 Quantum Computing Algorithms for Disaster Recovery Using Genetic and Fuzzy Adaptive Method
Nithish, P., Jayapandian, N., Sabu, A., & Cengiz, K. (2025) · De Gruyter eBooks · doi:10.1515/9783111619323-014
Flame Segmentation and Classification in Streaming Frames Using Optimized RUSBoost Method
Sruthi, S., & Anuradha, B. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-1587-2_44
RCM: A Novel Fire Detection Technique That Effectively Resists Interference in Complex Scenarios
Fang, Z., Jiang, X., Xu, L., & Fang, X. (2025) · IEEE Access · doi:10.1109/access.2025.3603263
“Muhammad et al. [9] utilized convolutional neural networks for early fire recognition.”
An Efficient Method for Object Detection with Automatic Illumination Correction
Kumar, C. K., Kumar, C. K., Sethi, G., & Rawal, K. (2025) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.130302
Ubiquitous Monitoring and Alert Fire Detection System in Data Center using Logistic Regression
ELFarra, B. K., & Salha, M. A. (2025) · Journal of Ubiquitous Computing and Communication Technologies · doi:10.36548/jucct.2025.3.003
Intelligent System for Crisis Analysis and Resource Allocation
Gorthi, L. S. A., Nadiminti, D. R. S., & Misha Chandar, B. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189655
“…Internet of Multimedia Things has enabled streaming architectures that combine real-time video with environmental sensors for early fire detection [4], these architectures typically assume reliable connectivity and sufficient edge-compute resources, which may not be available in field deployments.”
Chen, J., Duan, Y., Tang, H., & Li, X. (2025) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei68103.2025.11259299
“By improving the fully connected layer structure of AlexNet, they reduced the fire false alarm rate to 15% [9].”
Mitra, A., Paul, S., & Bhardwaj, R. (2025) · Advances in computers · doi:10.1016/bs.adcom.2025.08.001
Fire Detection and Suppression with an Automated Fire Extinguishing System
Dalla, V. K., Kumar, S., Singh, S., Kumar, N., Kumar, H., & Yadav, A. K. (2026) · Lecture notes in mechanical engineering · doi:10.1007/978-981-95-1872-2_52
Chien, C.-M., Trung, N. T. H., Cheng, C.-Y., Chen, C.-S., Yeh, D., & Wang, R.-Z. (2026) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-025-05019-6
Locality-Aware Backward Propagation for Deep Neural Networks
Yuan, S., Du, Q., & Tan, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5761-5_21
Artificial Intelligence Algorithms for Natural Disaster Management: Survey, Challenges
Sangeeta, Upadhyaya, S., Poriye, M., & Khavari, A. A. (2025) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon65210.2025.11341582
A Survey on Crime Detection in CCTV Surveillance: Image Processing and Deep Learning Approaches
Priya, J. J., & Priya, N. L. (2025) · International Conference on NexGen Networks and Cybernetics (IC2NC) · doi:10.1109/ic2nc67409.2025.11376290
Fire Detection in Surveillance Camera Systems Using a Deep-Learning-Based Approach
Nguyen, Q. T., Pham, M. K., Vu, K. T., Nguyen, H. H., & Thi, H. H. (2026) · Sustainable aviation · doi:10.1007/978-3-031-71085-8_30
Bhowmick, S. K., Barman, A., Roy, S. K., & Dutta, P. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21456-7
“High-performance convolutional neural networks (CNNs) have achieved state-of-the-art results across a wide range of computer vision tasks, including object detection, image classification, segmentation, and retrieval [52].”
Effective fire detection using deep learning based hybrid model
Priya, S. B., & Saranya, V. (2026) · AIP conference proceedings · doi:10.1063/5.0319935
InfernoFusion: An Edge-Optimized Audio-Visual Deep Learning Framework for Early Fire Smoke Detection
Asmitha, J. S., Dhanyasri, R., & Nivi, V. (2026) · International Conference on Visual Analytics and Data Visualization (ICVADV) · doi:10.1109/icvadv67766.2026.11470433
Surveillance Camera Driven Fire Detection for Smart Safety Systems
Rajesh, K. V., Amulya, C., Sri, B. D., Roopika, C., Rishitha, B., & Krishnaveni, D. (2026) · Contemporary Computing Innovations Conference (CCIC) · doi:10.1109/ccic68129.2026.11486169
“Recent studies employ CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks) for fire detection, enabling system to automatically learn complex fire patterns and reduce dependency on handcraft features [10].”
Deep Learning-Based Fire Detection with GPS Localization for Real-Time Monitoring
Deb, B. (2025) · IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON) · doi:10.1109/raaicon69033.2025.11502343
“Ecological and economic losses can be mitigated via autonomous response through early fire detection [3].”
Survey on Disaster Management Datasets for Remote Sensing-Based Emergency Applications
Ndigande, A. P., Wiggins, J., & Özer, S. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3692760
“Recently, DL models have shown remarkable success in learning intricate patterns and representations in many fields, including remote sensing, wireless communications, autonomous systems, and natural language processing [58], [86], [136], [184], [188], [207], [208], [213], [220].”
Xie, K., He, H., Zhu, J., Huang, D., Zhang, Q., & You, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115429
SFGANet: Smoke and Flame Global Aggregation Network
Wang, Y., Qiao, B., & Xie, G. (2026) · International Conference on Generative Artificial Intelligence and Information Security (GAIIS) · doi:10.1109/gaiis69281.2026.11519284
“These characteristics make robust detection in real-world scenes particularly challenging [1,2] .”
Proactive predictive analytics in cybersecurity using machine learning models: A review study
Idris, S., & Hamd, H. (2026) · AIP conference proceedings · doi:10.1063/5.0318606
Chen, G., Tananchana, A., Jiang, L., Zhou, X., Mu, L., & Deng, W. (2026) · Forests · doi:10.3390/f17060672
AI-powered smart building firefighting
Xie, W., Shi, J., Zhang, T., Zhang, X., & Huang, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36342-9.00012-8
Research on Aircraft Fire Detection Method Based on IATF-YOLO
Zhang, W., Wang, K., & Song, X. (2026) · Fire · doi:10.3390/fire9060255
“They can reliably identify fires at various scales and effectively reduce false and missed alarms [12].”
Medvei, M. M., Gurău, D. G., & Coca, M. (2026) · Future Internet · doi:10.3390/fi18070349
“Urban environmental monitoring using video feeds, including early fire and smoke detection [128,129,182], flood monitoring [183], and road condition assessment [184], involves analyzing data to assess current conditions, detect changes, and identify potential hazards, serving as a cornerstone of…”
HQDL: A Hybrid Quantum–Deep Learning Framework for Real-Time Fire Detection Using UAV Remote Sensing
Danish, S., Ullah, W., Fayaz, M., Dang, L. M., Song, H., & Moon, H. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3712753
“Earlier CNN-based methods such as ResNet50 [38], ANetFire [39], and CNNFire [40] achieve accuracies between 87 to 92%.”
Lai, D., Liu, L., Liu, J., Zhang, J., Du, X., Hao, R., & Wang, X. (2026) · Fire · doi:10.3390/fire9080340
Similarity Learning–Driven Knowledge Distillation for UAV-Based Disaster Detection
Khaja, I. A., Ahmad, M., & Ahmad, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19160-1_16
Lightweight Deep Learning Model for UAV-Based Disaster Detection
Ahmad, R., Akram, M. H., Ahmad, M., & Raza, K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-19160-1_15
Yar, H., Shah, N. A., Jiang, W., Alghamdi, N. S., & Kim, H. S. (2026) · Remote Sensing · doi:10.3390/rs18162706
Risk-Aware False-Alarm Suppression for Resource-Constrained Edge–Cloud Fire Detection
Zhao, L., Liu, Y., Yu, T., Ou, Y., Zhang, H., & Yu, X. (2026) · IEEE Access · doi:10.1109/access.2026.3724816
“Muhammad et al. [21] proposed a CNN-based early fire detection method for surveillance scenarios, improving fire recognition in complex monitoring environments.”
Yue, T., Huang, H., Song, B., Chen, Y., & Chen, Z. (2026) · Drones · doi:10.3390/drones10090662
“Therefore, early and accurate fire detection is of great significance for providing sufficient response time and reducing the potential damage caused by forest fires [3–5].”
Zakari, R. Y., Malik, O. A., & Wee-Hong, O. (2026) · Journal of Forestry Research · doi:10.1007/s11676-026-02130-y
“For example, Muhammad et al. (2018) introduced the Temporal Transformer Network (TTN), incorporating the self-attention mechanism for effective time series forecasting.”
Binary Classification of Forest Fire Images Through Deep Learning
Akermoud, A., Mahjoubi, M. A., & Aaluane, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30546-6_4
Fire Detection Using YOLO v8n-Tweaked
Khare, S., Chaurasia, B. K., Chandra, S., & Shukla, R. D. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-27343-7_6
Geospatial AI for Disaster Risk Intelligence and Real-Time Monitoring
Sachan, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1704-3.ch007
Autonomous Industrial Safety Surveillance Rover for Real-Time Fire and Gas Hazard Detection
Benny, A., Radhakrishnan, A., Prasanth, A., Krishnakumar, A., & Raju, S. P. (2026) · International Conference on Innovations in Mechanical, Robotics, Computing and Biomedical Engineering (ICIMRBE) · doi:10.1109/icimrbe70745.2026.11710088
Sultankulov, B., Nurtas, M., Chepashev, D., & Zhilkibayeva, E. (2026) · IEEE Access · doi:10.1109/access.2026.3741661
“This fundamental constraint introduces detection latency that renders such systems inadequate in large open industrial spaces, where fire may propagate significantly before sensors register a response [2].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
