Comparative Analysis of Deep Learning Algorithms in Fire Detection

Abstract

As technology advances rapidly, deep learning applications, a subset of machine learning, are becoming increasingly relevant in various aspects of our lives. Essential daily applications like license plate recognition and optical character recognition are now commonplace. Alongside current technological progress, the development of future-integrated technologies such as suspicious situation detection from security cameras and autonomous vehicles is also accelerating. The success and accuracy of these technologies have reached impressive levels. This study focuses on the early and accurate detection of forest fires before they cause severe damage. Using primarily forest fire images from datasets obtained from Kaggle, various deep learning algorithms were trained via transfer learning using MATLAB. This approach allowed for comparing different deep learning algorithms based on their efficiency and accuracy in detecting forest fires. High success rates, generally exceeding 90%, were achieved.

Keywords

• Fire Detection
• Image Processing
• Deep Learning
• Convolutional Neural Networks
• Deep Learning Algorithms

References

1. [1]. K. Avazov, M. Mukhiddinov, F. Makhmudov, and Y. I. Cho, “Fire detection method in smart city environments using a deep-learning-based approach,” Electron., vol. 11, no. 1, pp. 1–17, 2022, doi: 10.3390/electronics11010073.
2. [2]. C. Tao, J. Zhang, and P. Wang, “Smoke Detection Based on Deep Convolutional Neural Networks,” in 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII), Dec. 2016, pp. 150–153. doi: 10.1109/ICIICII.2016.0045.
3. [3]. P. Li and W. Zhao, “Image fire detection algorithms based on convolutional neural networks,” Case Stud. Therm. Eng., vol. 19, p. 100625, Jun. 2020, doi: 10.1016/j.csite.2020.100625.
4. [4]. K. Muhammad, J. Ahmad, I. Mehmood, S. Rho, and S. W. Baik, “Convolutional Neural Networks Based Fire Detection in Surveillance Videos,” IEEE Access, vol. 6, pp. 18174–18183, 2018, doi: 10.1109/ACCESS.2018.2812835.
5. [5]. G. Lindfield and J. Penny, “Numerical methods: Using MATLAB,” Numer. Methods Using MATLAB, pp. 1–608, 2018, doi: 10.1016/C2016-0-00395-9.
6. [6]. M. Cıbuk, U. Budak, Y. Guo, M. Cevdet Ince, and A. Sengur, “Efficient deep features selections and classification for flower species recognition,” Meas. J. Int. Meas. Confed., vol. 137, pp. 7–13, 2019, doi: 10.1016/j.measurement.2019.01.041
7. [7]. R. Daş, B. Polat, and G. Tuna, “Derin Öğrenme ile Resim ve Videolarda Nesnelerin Tanınması ve Takibi,” Fırat Üniversitesi Mühendislik Bilim. Derg., vol. 31, no. 2, pp. 571–581, 2019, doi: 10.35234/fumbd.608778.
8. [8]. C. Alippi, S. Disabato, and M. Roveri, “Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case,” in 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Apr. 2018, vol. 30, no. 2010, pp. 212–223. doi: 10.1109/IPSN.2018.00049.

9. [9]. H. Ismail Fawaz et al., “InceptionTime: Finding AlexNet for time series classification,” Data Min. Knowl. Discov., vol. 34, no. 6, pp. 1936–1962, 2020, doi: 10.1007/s10618-020-00710-y.
10. [10]. A. LeNail, “NN-SVG: Publication-Ready Neural Network Architecture Schematics,” J. Open Source Softw., vol. 4, no. 33, p. 747, Jan. 2019, doi: 10.21105/joss.00747.
11. [11]. S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” Int. J. Sci. Res. Publ., vol. 9, no. 10, p. p9420, 2019, doi: 10.29322/ijsrp.9.10.2019.p9420.
12. [12]. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.
13. [13]. M. Mateen, J. Wen, Nasrullah, S. Song, and Z. Huang, “Fundus Image Classification Using VGG-19 Architecture with PCA and SVD,” Symmetry (Basel)., vol. 11, no. 1, p. 1, Dec. 2018, doi: 10.3390/sym11010001.
14. [14]. G. Zeng, Y. He, Z. Yu, X. Yang, R. Yang, and L. Zhang, “Preparation of novel high copper ions removal membranes by embedding organosilane-functionalized multi-walled carbon nanotube,” J. Chem. Technol. Biotechnol., vol. 91, no. 8, pp. 2322–2330, 2016, doi: 10.1002/jctb.4820.
15. [15]. M. Längkvist, L. Karlsson, and A. Loutfi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” Pattern Recognit. Lett., vol. 42, no. 1, pp. 11–24, 2014, [Online]. Available: http://arxiv.org/abs/1512.00567
16. [16]. H.-J. Yoo, “Deep Convolution Neural Networks in Computer Vision: a Review,” IEIE Trans. Smart Process. Comput., vol. 4, no. 1, pp. 35–43, 2015, doi: 10.5573/ieiespc.2015.4.1.035.
17. [17]. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 2818–2826, 2016, doi: 10.1109/CVPR.2016.308.
18. [18]. C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.
19. [19]. “GoogLeNet evrişimli sinir ağı-MATLAB googlenet.” [Online]. Available: https://www.mathworks.com/help/deeplearning/ref/googlenet.html#mw_d60d4ed6-a2c9-44f1-93b8-977191c6cfea
20. [20]. M. Guo and Y. Du, “Classification of Thyroid Ultrasound Standard Plane Images using ResNet-18 Networks,” in 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), Oct. 2019, pp. 324–328. doi: 10.1109/ICASID.2019.8925267.
21. [21]. I. Z. Mukti and D. Biswas, “Transfer Learning Based Plant Diseases Detection Using ResNet50,” in 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Dec. 2019, pp. 1–6. doi: 10.1109/EICT48899.2019.9068805.
22. [22]. P. Ghosal, L. Nandanwar, S. Kanchan, A. Bhadra, J. Chakraborty, and D. Nandi, “Brain Tumor Classification Using ResNet-101 Based Squeeze and Excitation Deep Neural Network,” in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Feb. 2019, pp. 1–6. doi: 10.1109/ICACCP.2019.8882973.
23. [23]. J. Bobo, C. Hudley, and C. Michel, “The Black studies reader,” Black Stud. Read., pp. 1–488, 2004, doi: 10.4324/9780203491348.
24. [24]. R. Zhang et al., “Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets,” IEEE Trans. Med. Imaging, vol. 37, no. 9, pp. 2149–2160, 2018, doi: 10.1109/TMI.2018.2821244.
25. [25]. B. Khasoggi, E. Ermatita, and S. Samsuryadi, “Efficient mobilenet architecture as image recognition on mobile and embedded devices,” Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 1, p. 389, Oct. 2019, doi: 10.11591/ijeecs.v16.i1.pp389-394.
26. [26]. F. Saxen, P. Werner, S. Handrich, E. Othman, L. Dinges, and A. Al-Hamadi, “Face Attribute Detection with MobileNetV2 and NasNet-Mobile,” in 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Sep. 2019, pp. 176–180. doi: 10.1109/ISPA.2019.8868585.
27. [27]. R. H. Hridoy, F. Akter, M. Mahfuzullah, and F. Ferdowsy, “A Computer Vision Based Food Recognition Approach for Controlling Inflammation to Enhance Quality of Life of Psoriasis Patients,” in 2021 International Conference on Information Technology (ICIT), Jul. 2021, pp. 543–548. doi: 10.1109/ICIT52682.2021.9491783.
28. [28]. M. Nikhitha, S. Roopa Sri, and B. Uma Maheswari, “Fruit Recognition and Grade of Disease Detection using Inception V3 Model,” in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Jun. 2019, vol. 9, pp. 1040–1043. doi: 10.1109/ICECA.2019.8822095.
29. [29]. Xiaoling Xia, Cui Xu, and Bing Nan, “Inception-v3 for flower classification,” in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Jun. 2017, pp. 783–787. doi: 10.1109/ICIVC.2017.7984661.
30. [30]. Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271. 2017.
31. [31]. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” Apr. 2018, doi: 10.11772/j.issn.1001-9081.2018102190.
32. [32]. Huang, Xin, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang et al. "PP-YOLOv2: A practical object detector." arXiv preprint arXiv:2104.10419 (2021).
33. [33]. J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, pp. 248–255. doi: 10.7287/peerj.preprints.27880v1.
34. [34]. R. S. T. De Menezes, J. V. A. Luiz, A. M. Henrique-Alves, R. M. Santa Cruz, and H. Maia, “Mice Tracking Using The YOLO Algorithm,” pp. 162–173, 2020, doi: 10.5753/semish.2020.11326.
35. [35]. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 1800–1807. doi: 10.1109/CVPR.2017.195.
36. [36]. Y. Bhatia, A. Bajpayee, D. Raghuvanshi, and H. Mittal, “Image Captioning using Google’s Inception-resnet-v2 and Recurrent Neural Network,” in 2019 Twelfth International Conference on Contemporary Computing (IC3), Aug. 2019, pp. 1–6. doi: 10.1109/IC3.2019.8844921.
37. [37]. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: 10.1109/CVPR.2017.243.
38. [38]. K. Zhang, Y. Guo, X. Wang, J. Yuan, and Q. Ding, “Multiple Feature Reweight DenseNet for Image Classification,” IEEE Access, vol. 7, pp. 9872–9880, 2019, doi: 10.1109/ACCESS.2018.2890127.
39. [39]. F. Iandola, A. Shaw, R. Krishna, and K. Keutzer, “SqueezeBERT: What can computer vision teach NLP about efficient neural networks?,” in Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing, 2020, pp. 124–135. doi: 10.18653/v1/2020.sustainlp-1.17.
40. [40]. F. Özyurt, E. Sert, E. Avci, and E. Dogantekin, “Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy,” Measurement, vol. 147, p. 106830, Dec. 2019, doi: 10.1016/j.measurement.2019.07.058.
41. [41]. D. Ghimire, D. Kil, and S. Kim, “A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration,” Electronics, vol. 11, no. 6, p. 945, Mar. 2022, doi: 10.3390/electronics11060945.
42. [42]. S. I. Hossain et al., “Exploring convolutional neural networks with transfer learning for diagnosing Lyme disease from skin lesion images,” Comput. Methods Programs Biomed., vol. 215, p. 106624, Mar. 2022, doi: 10.1016/j.cmpb.2022.106624.
43. [43]. A. Saied, “FIRE Dataset,” Kaggle.com, 2018. https://www.kaggle.com/datasets/phylake1337/fire-dataset (accessed Jul. 30, 2024).
44. [44]. A. Kumar, “Fire Detection Dataset,” Kaggle.com, 2024. https://www.kaggle.com/datasets/atulyakumar98/test-dataset (accessed Jul. 30, 2024).
45. [45]. M. Burukanli, M. Çibuk, and Ü. Budak, “Saldırı Tespiti için Makine Öğrenme Yöntemlerinin Karşılaştırmalı Analizi Comparative Analysis of Machine Learning Methods for Intrusion Detection,” BEU J. Sci., vol. 10, no. 2, pp. 613–624, 2021.
46. [46]. R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” Int. Jt. Conf. Artif. Intell., no. March 2001, 1995.
47. [47]. F. Uyanık and M. C. and Kasapbaşı, “Telekomünikasyon Sektörü için Veri Madenciliği ve Makine Öğrenmesi Teknikleri ile Ayrılan Müşteri Analizi,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2021.