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References

1 
J.-F. Qiao , Z.-H. Guo , J. Tang , Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: A survey, Acta Automatica Sinica, Vol. 46, No. 6, pp. 1063-1089, June, 2020DOI
2 
E. de Titto , A. Savino , Environmental and health risks related to waste incineration, Waste Management & Research, Vol. 37, No. 10, pp. 976-986, July, 2019DOI
3 
S. Khokhlov , Z. Abiev , V. Makkoev , The choice of optical flame detectors for automatic explosion containment systems based on the results of explosion radiation analysis of methane-and dust-air mixtures, Applied Sciences, Vol. 12, No. 3, pp. 1515-1523, December, 2022DOI
4 
J. Ryu , D. Kwak , A study on flame and smoke detection algorithm using convolutional neural network based on deep learning, Journal of The Korean Society of Hazard Mitigation, Vol. 20, No. 1, pp. 223-232, February, 2020DOI
5 
P. Barmpoutis , T. Stathaki , K. Dimitropoulos , N. Grammalidis , Early fire detection based on aerial 360-degree sensors, deep convolution neural networks and exploitation of fire dynamic textures, Remote Sensing, Vol. 12, No. 19, pp. 3177-3201, August, 2020DOI
6 
Y.-Y. Zhao , J. Zhu , Y.-K. Xie , W.-L. Li , Y.-K. Guo , A real-time video flame detection algorithm based on improved YOLO-V3, Geomatics and Information Science of Wuhan University, Vol. 46, No. 3, pp. 326-334, March, 2021DOI
7 
Z.-S. Al Hakeem , H.-I. Shahadi , H.-H. Abbas , An automatic flame detection system for outdoor areas, TELKOMNIKA (Telecommunication Computing Electronics and Control), Vol. 21, No. 4, pp. 864-871, August, 2023DOI
8 
Y. Liu , A. Huang , Y. Luo , H. Huang , Y. Liu , Y. Chen , Q. Yang , Fedvision: An online visual object detection platform powered by federated learning, Proc. of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 08, pp. 13172-13179, April, 2020DOI
9 
C.-Z. Dong , F. N. Catbas , A review of computer vision-based structural health monitoring at local and global levels, Structural Health Monitoring, Vol. 20, No. 2, pp. 692-743, July, 2021DOI
10 
J. Zhao , R. Masood , S. Seneviratne , A review of computer vision methods in network security, IEEE Communications Surveys & Tutorials, Vol. 23, No. 3, pp. 1838-1878, June, 2021DOI
11 
G. Li , Y. Huang , Z. Chen , G.-D. Chesser , J.-L. Purswell , J. Linhoss , Y. Zhao , Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review, Sensors, Vol. 21, No. 4, pp. 1492-1495, January, 2021DOI
12 
J. Guo , H. He , T. He , L. Lausen , M. Li , H. Lin , Y. Zhu , GluonCV and GluonNLP: Deep learning in computer vision and natural language processing, The Journal of Machine Learning Research, Vol. 21, No. 1, pp. 845-851, February, 2020DOI
13 
J. Janai , F. Güney , A. Behl , A. Geiger , Computer vision for autonomous vehicles: Problems, datasets and state of the art, Foundations and Trends in Computer Graphics and Vision, Vol. 12, No. 1-3, pp. 1-308, July, 2020DOI
14 
Z. Wang , Q. She , T.-E. Ward , Generative adversarial networks in computer vision: A survey and taxonomy, ACM Computing Surveys, Vol. 54, No. 2, pp. 1-38, February, 2021DOI
15 
D. Bhatt , C. Patel , H. Talsania , J. Patel , R. Vaghela , S. Pandya , H. Ghayvat , CNN variants for computer vision: History, architecture, application, challenges and future scope, Electronics, Vol. 10, No. 20, pp. 2470-2475, September, 2021DOI
16 
H. Tian , T. Wang , Y. Liu , X. Qiao , Y. Li , Computer vision technology in agricultural automation-a review, Information Processing in Agriculture, Vol. 7, No. 1, pp. 1-19, March, 2020DOI
17 
M.-H. Guo , T.-X. Xu , J.-J. Liu , Z.-N. Liu , P.-T. Jiang , T.-J. Mu , S.-M. Hu , Attention mechanisms in computer vision: A survey, Computational Visual Media, Vol. 8, No. 3, pp. 331-368, March, 2022DOI
18 
P. Barmpoutis , P. Papaioannou , K. Dimitropoulos , N. Grammalidis , A review on early forest fire detection systems using optical remote sensing, Sensors, Vol. 20, No. 22, pp. 6442-6447, October, 2020DOI
19 
B. Kim , J. Lee , A video-based fire detection using deep learning models, Applied Sciences, Vol. 9, No. 14, pp. 2862-2873, July, 2019DOI
20 
H. Mokayed , T.-Z. Quan , L. Alkhaled , V. Sivakumar , Real-time human detection and counting system using deep learning computer vision techniques, Artificial Intelligence and Applications, Vol. 1, No. 4, pp. 221-229, October, 2023DOI