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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Intelligent and vision-based fire detection systems: A survey
Fengju Bu, Mohammad Samadi Gharajeh
Image and Vision Computing (2019) Vol. 91, pp. 103803-103803
Closed Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing
Panagiotis Barmpoutis, Periklis Papaioannou, Kosmas Dimitropoulos, et al.
Sensors (2020) Vol. 20, Iss. 22, pp. 6442-6442
Open Access | Times Cited: 384
A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms
Abdelmalek Bouguettaya, Hafed Zarzour, Amine Mohammed Taberkit, et al.
Signal Processing (2021) Vol. 190, pp. 108309-108309
Closed Access | Times Cited: 171
Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire
Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 720-720
Open Access | Times Cited: 68
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Sahand Khoshdel, et al.
Information Fusion (2024) Vol. 108, pp. 102369-102369
Open Access | Times Cited: 48
Detection of forest fire using deep convolutional neural networks with transfer learning approach
Hatice Çatal Reis, Veysel Turk
Applied Soft Computing (2023) Vol. 143, pp. 110362-110362
Closed Access | Times Cited: 45
Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach
Kuldoshbay Avazov, Mukhriddin Mukhiddinov, Fazliddin Makhmudov, et al.
Electronics (2021) Vol. 11, Iss. 1, pp. 73-73
Open Access | Times Cited: 87
A smart fire detection system using iot technology with automatic water sprinkler
Hamood Alqourabah, Amgad Muneer, Suliman Mohamed Fati
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2021) Vol. 11, Iss. 4, pp. 2994-2994
Open Access | Times Cited: 74
FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities
Somaiya Khan, Ali Khan
Symmetry (2022) Vol. 14, Iss. 10, pp. 2155-2155
Open Access | Times Cited: 64
A survey on vision-based outdoor smoke detection techniques for environmental safety
Shubhangi Chaturvedi, Pritee Khanna, Aparajita Ojha
ISPRS Journal of Photogrammetry and Remote Sensing (2022) Vol. 185, pp. 158-187
Closed Access | Times Cited: 60
Randomly Initialized CNN with Densely Connected Stacked Autoencoder for Efficient Fire Detection
Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U Min Ullah, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 116, pp. 105403-105403
Closed Access | Times Cited: 46
Development of early fire detection model for buildings using computer vision-based CCTV
Yusun Ahn, Haneul Choi, Byungseon Sean Kim
Journal of Building Engineering (2022) Vol. 65, pp. 105647-105647
Closed Access | Times Cited: 44
From forest to finished products: The contribution of Industry 4.0 technologies to the wood sector
Margherita Molinaro, Guido Orzes
Computers in Industry (2022) Vol. 138, pp. 103637-103637
Closed Access | Times Cited: 43
Preferred vector machine for forest fire detection
Xubing Yang, Zhichun Hua, Li Zhang, et al.
Pattern Recognition (2023) Vol. 143, pp. 109722-109722
Closed Access | Times Cited: 32
Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection
Bin Sun, Yan Li, Yangyang Zhang, et al.
Reliability Engineering & System Safety (2024) Vol. 248, pp. 110154-110154
Closed Access | Times Cited: 14
ESFD-YOLOv8n: Early Smoke and Fire Detection Method Based on an Improved YOLOv8n Model
Dilshodjon Mamadaliev, Philippe Lyonel Mbouembe Touko, Jae Ho Kim, et al.
Fire (2024) Vol. 7, Iss. 9, pp. 303-303
Open Access | Times Cited: 13
CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM
Yiqing Xu, Jiaming Li, Long Zhang, et al.
Fire (2024) Vol. 7, Iss. 2, pp. 54-54
Open Access | Times Cited: 12
Visual fire detection using deep learning: A survey
Guangtao Cheng, Xue Chen, Chenyi Wang, et al.
Neurocomputing (2024) Vol. 596, pp. 127975-127975
Closed Access | Times Cited: 12
Fire and smoke detection from videos: A literature review under a novel taxonomy
Diego Gragnaniello, Antonio Greco, Carlo Sansone, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124783-124783
Open Access | Times Cited: 9
Improved YOLOv5: Efficient Object Detection for Fire Images
Dongxing Yu, Shuchao Li, Zhongze Zhang, et al.
Fire (2025) Vol. 8, Iss. 2, pp. 38-38
Open Access | Times Cited: 1
Development of training image database using web crawling for vision-based site monitoring
Jeongbin Hwang, Jinwoo Kim, Seokho Chi, et al.
Automation in Construction (2022) Vol. 135, pp. 104141-104141
Open Access | Times Cited: 37
Video Fire Detection Methods Based on Deep Learning: Datasets, Methods, and Future Directions
C Jin, Tao Wang, Naji Alhusaini, et al.
Fire (2023) Vol. 6, Iss. 8, pp. 315-315
Open Access | Times Cited: 23
Toward Efficient Fire Detection in IoT Environment: A Modified Attention Network and Large-Scale Data Set
Naqqash Dilshad, Samee U. Khan, Norah Saleh Alghamdi, et al.
IEEE Internet of Things Journal (2023) Vol. 11, Iss. 8, pp. 13467-13481
Closed Access | Times Cited: 17
TobSet: A New Tobacco Crop and Weeds Image Dataset and Its Utilization for Vision-Based Spraying by Agricultural Robots
Muhammad Shahab Alam, Mansoor Alam, Muhammad Tufail, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1308-1308
Open Access | Times Cited: 26
A Survey on Monitoring of Wild Animals during Fires Using Drones
Светлана Иванова, Alexander Prosekov, Anatoly Kaledin
Fire (2022) Vol. 5, Iss. 3, pp. 60-60
Open Access | Times Cited: 25
A Yolo-based Approach for Fire and Smoke Detection in IoT Surveillance Systems
Dawei Zhang
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
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