Research Article
BibTex RIS Cite
Year 2021, , 84 - 94, 31.07.2021
https://doi.org/10.22399/ijcesen.950045

Abstract

References

  • [1] Den Breejen, E., Breuers, M., Cremer, F., Kemp, R., Roos, M., Schutte, K., De Vries, J. S. (1998). Autonomous forest fire detection (pp. 2003-2012). Coimbra, Portugal: ADAI-Associacao para o Desenvolvimento da Aerodinamica Industrial.
  • [2] Thengade, A., Mishra, P., Kshatriya, R., Mhaskar, R., & Bodhe, P. Fire Detection Using Image Processing Using Raspberry PI.
  • [3] Kilimci, Z. H., Ganiz, M. C. (2015, September). Evaluation of classification models for language processing. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.
  • [4] Thengade, A., Mishra, P., Kshatriya, R., Mhaskar, R., & Bodhe, P. Fire Detection Using Image Processing Using Raspberry PI
  • [5] Deng, L., Hinton, G., Kingsbury, B. (2013, May). New types of deep neural network learning for speech recognition and related applications: An overview. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8599-8603). IEEE.
  • [6] Singh, S. P., Kumar, A., Darbari, H., Singh, L., Rastogi, A., Jain, S. (2017, July). Machine translation using deep learning: An overview. In 2017 international conference on computer, communications and electronics (comptelix) (pp. 162-167). IEEE.
  • [7] Khondaker, A., Khandaker, A., Uddin, J. (2020). Computer Vision-based Early Fire Detection Using Enhanced Chromatic Segmentation and Optical Flow Analysis Technique. International Arab Journal Of Information Technology, 17(6), 947-953.
  • [8] A. Rafiee, R. Dianat, M. Jamshidi, R. Tavakoli, and S. Abbaspour, Fire and smoke detection using wavelet analysis and disorder characteristics, ICCRD 2011 - 2011 3rd Int. Conf. Comput. Res. Dev., 3 (2011) 262–265.
  • [9] Mahmoud, M. A. I., Ren, H. (2019). Forest fire detection and identification using image processing and SVM. Journal of Information Processing Systems, 15(1), 159-168.
  • [10] Sadewa, R. P., Irawan, B., Setianingsih, C. (2019, December). Fire detection using image processing techniques with convolutional neural networks. In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 290-295). IEEE.
  • [11] Sucuoğlu, H. S., Böğrekçi, İ., Demircioğlu, P. Real Time Fire Detection Using Faster R-CNN Model. International Journal of 3D Printing Technologies and Digital Industry, (2019). 3(3), 220-226.
  • [12] Mwedzi, N. A., Nwulu, N. I., Gbadamosi, S. L. (2019, December). Machine Learning Applications for Fire Detection in a Residential Building. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-4). IEEE.
  • [13] Abid, F. (2021). A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technology, 57(2), 559-590.
  • [14] Xu, R., Lin, H., Lu, K., Cao, L., Liu, Y. (2021). A Forest Fire Detection System Based on Ensemble Learning. Forests, 12(2), 217.
  • [15] Pourghasemi, H. R., Gayen, A., Lasaponara, R., & Tiefenbacher, J. P. (2020). Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. Environmental research, 184, 109321.7
  • [16] Maksymiv, O., Rak, T., Peleshko, D. (2017, February). Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence. In 2017 14th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM) (pp. 351-353). IEEE.
  • [17] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • [18] Fukushima, K., Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets (pp. 267-285). Springer, Berlin, Heidelberg.
  • [19] Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • [20] Azarmdel, H., Jahanbakhshi, A., Mohtasebi, S. S., & Muñoz, A. R. (2020). Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biology and Technology, 166, 111201.
  • [21] Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE.
  • [22] Ren, S., He, K., Girshick, R., Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.
  • [23] Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., & Hemanth, J. (2021). SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2. Sustainable cities and society, 66, 102692.
  • [24] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [25] Shashua, A. (2009). Introduction to machine learning: Class notes 67577. arXiv preprint arXiv:0904.3664.
  • [26] https://cs231n.github.io/convolutional-networks/

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Year 2021, , 84 - 94, 31.07.2021
https://doi.org/10.22399/ijcesen.950045

Abstract

Forest fire detection is a very challenging problem in the field of object detection. Fire detection-based image analysis have advantages such as usage on wide open areas, the possibility for operator to visually confirm presence, intensity and the size of the hazards, lower cost for installation and further exploitation. To overcome the problem of fire detection in outdoors, deep learning and conventional machine learning based computer vision techniques are employed to determine the fire detection when indoor fire detection systems are not capable. In this work, we propose a comprehensive analysis of forest fire detection using conventional machine learning algorithms, object detection techniques, deep and hybrid deep learning models. Experiment results demonstrate that convolutional neural networks outperform other methods with 99.32% of accuracy result.

References

  • [1] Den Breejen, E., Breuers, M., Cremer, F., Kemp, R., Roos, M., Schutte, K., De Vries, J. S. (1998). Autonomous forest fire detection (pp. 2003-2012). Coimbra, Portugal: ADAI-Associacao para o Desenvolvimento da Aerodinamica Industrial.
  • [2] Thengade, A., Mishra, P., Kshatriya, R., Mhaskar, R., & Bodhe, P. Fire Detection Using Image Processing Using Raspberry PI.
  • [3] Kilimci, Z. H., Ganiz, M. C. (2015, September). Evaluation of classification models for language processing. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.
  • [4] Thengade, A., Mishra, P., Kshatriya, R., Mhaskar, R., & Bodhe, P. Fire Detection Using Image Processing Using Raspberry PI
  • [5] Deng, L., Hinton, G., Kingsbury, B. (2013, May). New types of deep neural network learning for speech recognition and related applications: An overview. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 8599-8603). IEEE.
  • [6] Singh, S. P., Kumar, A., Darbari, H., Singh, L., Rastogi, A., Jain, S. (2017, July). Machine translation using deep learning: An overview. In 2017 international conference on computer, communications and electronics (comptelix) (pp. 162-167). IEEE.
  • [7] Khondaker, A., Khandaker, A., Uddin, J. (2020). Computer Vision-based Early Fire Detection Using Enhanced Chromatic Segmentation and Optical Flow Analysis Technique. International Arab Journal Of Information Technology, 17(6), 947-953.
  • [8] A. Rafiee, R. Dianat, M. Jamshidi, R. Tavakoli, and S. Abbaspour, Fire and smoke detection using wavelet analysis and disorder characteristics, ICCRD 2011 - 2011 3rd Int. Conf. Comput. Res. Dev., 3 (2011) 262–265.
  • [9] Mahmoud, M. A. I., Ren, H. (2019). Forest fire detection and identification using image processing and SVM. Journal of Information Processing Systems, 15(1), 159-168.
  • [10] Sadewa, R. P., Irawan, B., Setianingsih, C. (2019, December). Fire detection using image processing techniques with convolutional neural networks. In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 290-295). IEEE.
  • [11] Sucuoğlu, H. S., Böğrekçi, İ., Demircioğlu, P. Real Time Fire Detection Using Faster R-CNN Model. International Journal of 3D Printing Technologies and Digital Industry, (2019). 3(3), 220-226.
  • [12] Mwedzi, N. A., Nwulu, N. I., Gbadamosi, S. L. (2019, December). Machine Learning Applications for Fire Detection in a Residential Building. In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-4). IEEE.
  • [13] Abid, F. (2021). A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems. Fire Technology, 57(2), 559-590.
  • [14] Xu, R., Lin, H., Lu, K., Cao, L., Liu, Y. (2021). A Forest Fire Detection System Based on Ensemble Learning. Forests, 12(2), 217.
  • [15] Pourghasemi, H. R., Gayen, A., Lasaponara, R., & Tiefenbacher, J. P. (2020). Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling. Environmental research, 184, 109321.7
  • [16] Maksymiv, O., Rak, T., Peleshko, D. (2017, February). Real-time fire detection method combining AdaBoost, LBP and convolutional neural network in video sequence. In 2017 14th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM) (pp. 351-353). IEEE.
  • [17] Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • [18] Fukushima, K., Miyake, S. (1982). Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets (pp. 267-285). Springer, Berlin, Heidelberg.
  • [19] Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
  • [20] Azarmdel, H., Jahanbakhshi, A., Mohtasebi, S. S., & Muñoz, A. R. (2020). Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biology and Technology, 166, 111201.
  • [21] Ho, T. K. (1995, August). Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition (Vol. 1, pp. 278-282). IEEE.
  • [22] Ren, S., He, K., Girshick, R., Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.
  • [23] Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., & Hemanth, J. (2021). SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2. Sustainable cities and society, 66, 102692.
  • [24] Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • [25] Shashua, A. (2009). Introduction to machine learning: Class notes 67577. arXiv preprint arXiv:0904.3664.
  • [26] https://cs231n.github.io/convolutional-networks/
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Süha Berk Kukuk 0000-0003-1651-2417

Zeynep Hilal Kilimci 0000-0003-1497-305X

Publication Date July 31, 2021
Submission Date June 9, 2021
Acceptance Date July 6, 2021
Published in Issue Year 2021

Cite

APA Kukuk, S. B., & Kilimci, Z. H. (2021). Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 7(2), 84-94. https://doi.org/10.22399/ijcesen.950045