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2024 | OriginalPaper | Buchkapitel

A Deep Learning CNN Approach Regarding Drone Surveillance in Fire-Fighting Scenarios

verfasst von : Ana-Maria Travediu, Luige Vladareanu, Radu Munteanu, Jianye Niu, Daniel Octavian Melinte, Ionel Pușcașu

Erschienen in: Advances in Emerging Information and Communication Technology

Verlag: Springer Nature Switzerland

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Abstract

This article presents the development of a computer vision module for UAVs engaged in firefighting scenarios. The module features two deep neural networks trained on a customized database, which contains three classes of images: fire, smoke, and person. The aim is to give first responders details such as the number of human victims, their state and their positions, and the type of fuel that keeps the fire going, which helps firefighters to better prioritize their actions in a fire scenario and make the intervention safer for them as well. A faster RCNN and an SSD are used in order to detect these three classes, and the best model is then used to help first responders. The model achieves a precision of 0.58 for 50 IoU, 0.68 for the fire class, 0.68 for the person class, 0.50 for the smoke class because of smoke opacity. Even though the confidence score was high in detections, having false detections, especially with the smoke class, made a low precision overall.

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A. Bouguettaya, H. Zarzour, A.M. Taberkit, A. Kechida, A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms. Sig. Process. (2021). https://doi.org/10.1016/j.sigpro.2021.108309

Z. Tang, X. Liu, H. Chen, J. Hupy, B. Yang, Deep learning based wildfire event object detection from 4K aerial images acquired by UAS. AI 1, 166–179 (2020). https://doi.org/10.3390/ai1020010CrossRef

J.J. Roldán-Gómez, E. González-Gironda, A. Barrientos, A survey on robotic technologies for forest firefighting: Applying drone swarms to improve firefighters’ efficiency and safety. Appl. Sci. (2021). https://doi.org/10.3390/app11010363

Á. Madridano, A. Al-Kaff, P. Flores, D. Martín, A. de la Escalera, Software architecture for autonomous and coordinated navigation of UAV swarms in forest and urban firefighting. Appl. Sci. 11, 1258 (2021). https://doi.org/10.3390/app11031258CrossRef

A. Gupta, A. Bhatnagar, A. Mehta, Application of drones in maritime industry (Fire fighting). Bull. Mar. Sci. Technol. 15, 59–69. ISSN: 0974–8474

B. Aydin, E. Selvi, J. Tao, M.J. Starek, Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting. Drones 3, 17 (2019). https://doi.org/10.3390/drones3010017CrossRef

J.-H. Kim, S. Jo, B.Y. Lattimer, Feature selection for intelligent firefighting robot classification of fire, smoke, and thermal reflections using thermal infrared images. J. Sens. (Hindawi Publishing Corporation) 2016, Article ID 8410731, 13 pages (2016). https://doi.org/10.1155/2016/8410731CrossRef

P.-F. Tsai, C.-H. Liao, S.-M. Yuan, Using deep learning with thermal imaging for human detection in heavy smoke scenarios. Sensors 22, 5351 (2022). https://doi.org/10.3390/s22145351CrossRef

10.

H.-S. Choi, Automatic Fire Fighting Apparatus using Image Process of Deep Learning (Department of Computational Science and Technology, Seoul National University, 2020)

11.

M.I. Mohd Aliff, N.S. Yusof, A.Z. Sani, Development of fire fighting robot (Qrob). Int. J. Adv. Comput. Sci. Appl. 10(1) (2019). https://doi.org/10.14569/IJACSA.2019.0100118

12.

M. Bhattarai, M. Martı́nez-Ramón, A deep Q-learning based path planning and navigation system for firefighting environments. arXiv:2011.06450v1 [cs.AI] (2020)

13.

M. Bhattarai, Integrating deep learning and augmented reality to enhance situational awareness in firefighting environments. The University of New Mexico. arXiv: 2107.11043v2 [cs.CV] (2021)

14.

A. Dhiman, N. Shah, P. Adhikari, S. Kumbhar, I.S. Dhanjal, N. Mehendale, Fire Fighter Robot with Deep Learning and Machine Vision. SSRN 15 July 2020

15.

V. Nunavath, A. Prinz, T. Comes, Identifying first responders information needs: Sup- porting search and rescue operations for fire emergency response. Int. J. Inf. Syst. Crisis Response Manag. 8, 25–46 (2016). https://doi.org/10.4018/IJISCRAM.2016010102CrossRef

16.

Y. Ban, P. Zhang, A. Nascetti, A.R. Bevington, M.A. Wulder, Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning. Sci. Rep. (2020). https://doi.org/10.1038/s41598-019-56967-x

17.

A. Shamsoshoara, F. Afghah, A. Razi, L. Zheng, P.Z. Fulé, E. Blasch, Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset. arXiv:2012.14036v1 (2020)

18.

Z. Hong, Z. Tang, H. Pan, Y. Zhang, Z. Zheng, R. Zhou, Z. Ma, Y. Zhang, Y. Han, J. Wang, S. Yang, Active fire detection using a novel convolutional neural network based on Himawari-8 satellite images. Sec. Environ. Inf. Remote Sens. (2022). https://doi.org/10.3389/fenvs.2022.794028

19.

A. Larsen, I. Hanigan, B.J. Reich, Y. QinI, M. CopeI, G. Morgan, A.G. Rappold, A deep learning approach to identify smoke plumes in satellite imagery in near real-time for health risk communication. J. Ex-po Sci. Environ. Epidemiol. 31(1), 170–176 (2021). https://doi.org/10.1038/s41370-020-0246-yCrossRef

20.

https://www.military.com/veteran-jobs/career-advice/transition-to-firefighting-from-military-service.html

21.

https://developers.arcgis.com/python/guide/how-ssd-works/

22.

https://www.geeksforgeeks.org/inception-v2-and-v3-inception-network-versions/

23.

https://towardsdatascience.com/a-simple-guide-to-the-versions-of-the-inception-network-7fc52b863202

24.

https://developer.ridgerun.com/wiki/images/e/eb/Googlenet.png

25.

https://towardsdatascience.com/review-ssd-single-shot-detector-object-detection-851a94607d11

26.

https://towardsdatascience.com/fast-r-cnn-for-object-detection-a-technical-summary-a0ff94faa02

27.

https://towardsdatascience.com/faster-r-cnn-for-object-detection-a-technical-summary-474c5b857b46

28.

https://paperswithcode.com/method/inception-resnet-v20

Titel: A Deep Learning CNN Approach Regarding Drone Surveillance in Fire-Fighting Scenarios
verfasst von: Ana-Maria Travediu
Luige Vladareanu
Radu Munteanu
Jianye Niu
Daniel Octavian Melinte
Ionel Pușcașu
Verlag: Springer Nature Switzerland
Buch: Advances in Emerging Information and Communication Technology
Print ISBN: 978-3-031-53236-8

Electronic ISBN: 978-3-031-53237-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53237-5_12

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