APPLICATION OF THE VGG-16 CONVOLUTIONAL NEURAL NETWORK FOR THE INTELLIGENT ANALYSIS OF FOREST FIRE IMAGERY

T. H. Nguyen, T. L. Nguyen, A. G. Shmeleva

Abstract


Deep learning application projects face numerous challenges, with many problems requiring optimal solutions to improve system performance. We can focus on a specific step or the entire process. The goal of this work is to demonstrate a novel approach using convolutional neural networks for intelligent image processing. Due to the impact of climate change, natural disasters are becoming more complex and have severe consequences. In particular, the phenomenon of forest fires is influenced by factors such as humidity, temperature, vegetation characteristics, etc., making their detection and prediction especially challenging. In this study, we focus on using image processing techniques and visual algorithms for data preprocessing and noise filtering to create a comprehensive database for model training and testing. Natural elements are also combined with object features (forest fire images) to build feature vectors. This research will contribute to technological advancement and create new opportunities for subsequent applications.


Keywords


deep learning; forest fire; image processing; VGG-16; convolutional neural network; artificial intelligence

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References


Kulikov A.A. the Model Is a Reprint of an Object in the Image. Russian Technological Journal, 2020, vol. 8, no. 3, pp. 7-13. DOI: 10.32362/2500-316X-2020-8-3-7-13

Kulikov A.A. Application of Biometric Systems in Face Identification Technologies. Russian Technological Journal, 2021, vol. 9, no. 1, pp. 7-14. DOI: 10.32362/2500-316x-2021-9-3-7-14

Alves J., Soares C., Torres J.M., Sobral P., Moreira R.S. Automatic Forest Fire Detection Based on a Machine Learning and Image Analysis Pipeline. In: Rocha A., Adeli H., Reis L.P., Costanzo S. (eds.) New Knowledge in Information Systems and Technologies. WorldCIST 2019. Advances in Intelligent Systems and Computing, Springer, Cham, 2019, vol. 931, pp. 240-251. DOI: 10.1007/978-3-030-16184-2_24

Arridge S., Hauptmann A. Networks for Nonlinear Diffusion Problems in Imaging. Journal of Mathematical Imaging and Vision, 2020, vol. 62, no. 3, pp. 471-487. DOI: 10.1007/s10851-019-00901-3

Bangare S., Dubal A., Bangare P., Patil S. Reviewing OTSU Method for Image Thresholding. International Journal of Applied Engineering Research, 2015, vol. 10, no. 9, pp. 21777-21783.

Bhanumathi V., Sangeetha R. CNN Based Training and Classification of MRI Brain Images. In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 2019, pp. 129-133. DOI: 10.1109/ICACCS.2019.8728453

Browne M., Ghidary S.S. Convolutional Neural Networks for Image Processing: An Application in Robot Vision. In: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science, vol. 12568, pp. 641-652. Springer, Cham (2020).

Chen Q., Xu J., Koltun V. Fast Image Processing With Fully-Convolutional Networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 235-250. DOI: 10.1109/ICCV.2017.273

Nguyen T.H., Nguyen T.L., Sidorov D.N., Dreglea A.I. Machine Learning Algorithms Application to Road Defects Classification. Intelligent Decision Technologies, 2018, vol. 12, no. 1, pp. 1-8. DOI: 10.3233/IDT-170323.

Wang L. Support Vector Machines: Theory and Applications. Springer Verlag, Berlin, Heidelberg, 2005. DOI: 10.1007/b95439

Fujiwara N., Terada K. Extraction of a Smoke Region Using Fractal Coding. IEEE International Symposium on Communications and Information Technology, 2004. vol. 2, pp. 659-662. DOI: 10.1109/ISCIT.2004.1413797

Huang X., Shan J., Vaidya V. Lung Nodule Detection in CT Using 3D Convolutional Neural Networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, 2017, pp. 379-383. DOI: 10.1109/ISBI.2017.7950542

Hui T.H.A., Xin L., Jie C., Hongyong Y. Multi-Type Flame Detection Combined With Faster R-CNN. Journal of Image and Graphics, 2019, vol. 24, no. 1, pp. 73-83.

Zhao J., Zhong Z., Han Z., Lu Y. SVM Based Forest Fire Detection Using Static and Dynamic Features. Computer Science and Information Systems, 2011, vol. 8, no. 3, pp. 821-841. DOI: 10.2298/CSIS101231030Z

Zhao W., Zhang H., Fu Y., Zheng J., Zhang X., He L. A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC. Applied Sciences, 2018, vol. 8, no. 4, pp. 501-525. DOI: 10.3390/app8040501


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