پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,027 |
| تعداد مشاهده مقاله | 67,082,790 |
| تعداد دریافت فایل اصل مقاله | 7,656,233 |
A real-time forest fire and smoke detection system using deep learning | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 170، دوره 13، شماره 1، خرداد 2022، صفحه 2053-2063 اصل مقاله (528.08 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5899 | ||
| نویسنده | ||
| Raghad K. Mohammed* | ||
| Department of Basic Sciences, College of Dentistry, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 13 شهریور 1400، تاریخ بازنگری: 20 آبان 1400، تاریخ پذیرش: 28 آبان 1400 | ||
| چکیده | ||
| Large parts of the world's forests are threatened by fires. These fires happen continuously every month around the globe. They are very costly to society and cause serious damage to the ecosystem. This raises the necessity to build a detection system to intervene early and take action. Fire and smoke have various colours, textures, and shapes, which are challenging to detect. In the modern world, neural networks are used extensively in most fields of human activities. For the detection of fire and smoke, we suggest a deep learning technology using transfer learning to extract features of forest fire and smoke. We used a pre-trained Inception-ResNet-v2 network on the ImageNet dataset to be trained on our dataset which consists of 1,102 images for each fire and smoke class. The classification accuracy, precision, recall, F1-Score, and specificity were 99.09\%, 100\%, 98.08\%, 99.09\%, and 98.30\%, respectively. This model has been deployed on a Raspberry Pi device with a camera. For real-time detection, we used the Open CV library to read the camera stream frame by frame and predict the probability of fire or smoke. | ||
| کلیدواژه‌ها | ||
| Convolutional neural networks؛ deep learning؛ object detection؛ smoke detection؛ fire detection؛ transfer learning | ||
| مراجع | ||
|
[1] M. Byra, Discriminant analysis of neural style representations for breast lesion classification in ultrasound, Biocyber. Biomed. Engin. 38(3) (2018) 684–690. [2] J. Z. Cheng, D. Ni, Y.H. Chou, J. Qin, C.M. Tiu, Y.C. Chang, C.S. Huang, D. Shen and C.M. Chen, Computeraided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary ndules in CT scans, Scientific Rep. 6(1) (2016) 1–13. [3] F. Cui, Deployment and integration of smart sensors with IoT devices detecting fire disasters in huge forest environment, Computer Commun. 150 (2020) 818–827. [4] K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recogn. (2016) 770–778. [5] P. Jain, S.C.P. Coogan, S.G. Subramanian, M. Crowley, S. Taylor and M.D. Flannigan, A review of machine learning applications in wildfire science and management, In Environmental Reviews, 28(4) (2020) 478–505. [6] Z. Jiao, Y. Zhang, L. Mu, J. Xin, S. Jiao, H. Liu and D. Liu, A YOLOv3-based Learning Strategy for Real-time UAV-based Forest Fire Detection, Proc. 32nd Chinese Control Decision Conf., IEEE, (2020) 4963–4967. [7] Z. Jiao, Y. Zhang, J. Xin, L. Mu, Y. Yi, H. Liu and D. Liu, A Deep learning based forest fire detection approach using uav and yolov3, 1st Int. Conf. Indust. Artif. Intell. (2019) 1–5. [8] A. Krizhevsky, I. Sutskever and G.E. Hinton, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60(6) (2017) 84–90. [9] S.B. Kukuk and Z.H. Kilimci, Comprehensive analysis of forest fire detection using deep learning models and conventional machine learning algorithms, Int. J. Comput. Experimental Sci. Engin. 7(2) (2021) 84–94. [10] Y. LeCun, K. Kavukcuoglu and C. Farabet, Convolutional networks and applications in vision, ISCAS 2010-2010 IEEE Int. Symp. Circ. Syst.: Nano-Bio Circuit Fabrics Syst. (2010) 253–256. [11] T. Y. Lin, P. Doll´ar, R. Girshick, K. He, B. Hariharan and S. Belongie, Feature pyramid networks for object detection, Proc. 30th IEEE Conf. Computer Vision and Pattern Recogn. Janua 2017 936–944. [12] G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. van der Laak, B. van Ginneken and C.I. S´anchez, A survey on deep learning in medical image analysis, Medical Image Anal. 42 (2017) 60–88. [13] T. Liu, J. Cheng, X. Du, X. Luo,L. Zhang, B. Cheng and Y. Wang, Video smoke detection method based on change-cumulative image and fusion deep network, Sensors 19(23) (2019) 50–60. [14] B.S. Negara, R. Kurniawan, M.Z.A. Nazri, S.N.H.S. Abdullah, R.W. Saputraand and A. Ismanto, Riau forest fire prediction using supervised machine learning, J. Phys.: Conf. Ser. 1566(1) (2020) 12–20. [15] Y. Peng and Y. Wang,Real-time forest smoke detection using hand-designed features and deep learning, Comput. Electr. Agricul. 167 (2019) 105029. [16] H. Pranamurti, A. Murti and C. Setianingsih, Fire Detection Use CCTV with Image Processing Based Raspberry Pi, J. Phys.: Conf. Ser. 1201(1) (2019) 012015. [17] M. Rahul, K. Shiva Saketh, A. Sanjeet and N. Srinivas Naik, Early detection of forest fire using deep learning, IEEE Region 10 Annual Int. Conf. (2020) 1136–1140. [18] T. Schoennagel, J.K. Balch, H. Brenkert-Smith, P.E. Dennison, B.J. Harvey, M.A. Krawchuk, N. Mietkiewicz, P. Morgan, M.A. Moritz, R. Rasker, M.G. Turner and C. Whitlock, Adapt tomore wildfire in western North American forests as climate changes, Proc. Nat. Acad. Sci. United States Amer. 114(18) (2017) 4582–4590. [19] A. Shamsoshoara, F. Afghah, A. Razi, L. Zheng,P. Z. Ful´e and E. Blasch, Aerial imagery pile burn detection using deep learning: The FLAME dataset, Computer Networks 193 (2021) 108001. [20] D. Shen, G. Wu and H. Suk, Deep Learning in Medical Image Analysis, Annual Rev. Biomed. Engin. 19 (2017) 221–248. [21] G. Shi, H. Yan, W. Zhang, J. Dodson, H. Heijnis and M. Burrows, Rapid warming has resulted in more wildfires in northeastern Australia, Sci. Total Envir. 771 (2020) 144888. [22] H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura and R.M. Summers, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Medical Imag. 35(5) (2016) 1285-1298. [23] C. Szegedy, S. Ioffe, V. Vanhoucke and A.A. Alemi, Inception-v4, inception-ResNet and the impact of residual connections on learning, 31st AAAI Conf. Artificial Intell. (2017) 4278–4284. [24] L. Vil`a-Vilardell, W. S. Keeton, D. Thom, C. Gyeltshen, K. Tshering and G. Gratzer, Climate change effects on wildfire hazards in the wildland-urban-interface – Blue pine forests of Bhutan, Forest Eco. Manag. 461 (2020) 117927. [25] K. Weiss, T.M. Khoshgoftaar and D.D. Wang, A survey of transfer learning, J. Big Data 3(1) (2016) 1–40. [26] R. Xu, H. Lin, K. Lu, L. Cao and Y. Liu, A forest fire detection system based on ensemble learning, Forests 12(2) (2021) 1–17. [27] V. Yaloveha, D. Hlavcheva and A. Podorozhniak, Usage of convolutional neural network for multispectral image processing applied to the problem of detecting fire hazardous forest areas, Adv. Inf. Syst. 3(1) (2019) 116–120. | ||
|
آمار تعداد مشاهده مقاله: 17,682 تعداد دریافت فایل اصل مقاله: 2,655 |
||