Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

Djarot Hindarto

doi:10.47709/cnahpc.v6i1.3371

Authors

Djarot Hindarto Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta

DOI:

10.47709/cnahpc.v6i1.3371

Keywords:

CNN, VGG16, Forest Fire Detection, Transfer Learning, Visual Pattern Recognition

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Abstract

The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.

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References

Alzu, A., & Alsmadi, L. (2022). Ecological Informatics Monitoring deforestation in Jordan using deep semantic segmentation with satellite imagery. 70(July), 10–20.

Cinyol, F., Baysal, U., Köksal, D., Babao?lu, E., & Sar?nç, S. (2023). Incorporating support vector machine to the classification of respiratory sounds by Convolutional Neural Network. 79(August 2022).

Duarte, K. T. N., Gobbi, D. G., Sidhu, A. S., Mccreary, C. R., Saad, F., Camicioli, R., Smith, E. E., & Frayne, R. (2023). Segmenting white matter hyperintensities in brain magnetic resonance images using convolution neural networks. 175(August), 90–94.

He, L., Gong, X., Zhang, S., Wang, L., & Li, F. (2021). Neurocomputing Efficient attention based deep fusion CNN for smoke detection in fog environment. 434, 224–238.

Hindarto, D. (2023a). Battle Models?: Inception ResNet vs . Extreme Inception for Marine Fish Object Detection. 8(4), 2819–2826.

Hindarto, D. (2023b). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. 8(4), 2810–2818.

Hindarto, D. (2023c). Performance Comparison ConvDeconvNet Algorithm Vs . UNET for Fish Object Detection. 8(4), 2827–2835.

Hindarto, D. (2023d). Revolutionizing Automotive Parts Classification Using InceptionV3 Transfer Learning. 3(December), 324–333.

Hindarto, D., & Afarini, N. (2023). COMPARISON EFFICACY OF VGG16 AND VGG19 INSECT CLASSIFICATION. 6(3), 189–195. https://doi.org/10.33387/jiko.v6i3.7008

Hindarto, D., & Amalia, N. (2023). Implementation of Flower Recognition using Convolutional Neural Networks. 3(December), 341–351.

Hindarto, D., Program, I. S., Technology, I., & Nasional, U. (2023). Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species. 8(4), 2776–2785.

Jha, S. B., & Babiceanu, R. F. (2023). Deep CNN-based visual defect detection?: Survey of current literature. 148(January).

Kumar, S., & Kumar, H. (2023). MethodsX Classification of COVID-19 X-ray images using transfer learning with visual geometrical groups and novel sequential convolutional neural networks. 11(December 2022), 1–15.

Majid, S., Alenezi, F., Masood, S., Ahmad, M., Selda, E., & Polat, K. (2022). Attention based CNN model for fire detection and localization in real-world images. 189(October 2021).

Sharma, S., Guleria, K., Tiwari, S., & Kumar, S. (2022). A deep learning based convolutional neural network model with VGG16 feature extractor for the detection of Alzheimer Disease using MRI scans. 24(September).

Sun, J., Yang, F., Cheng, J., Wang, S., & Fu, L. (2024). Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM. Journal of Food Composition and Analysis, 125(June 2023), 105713. https://doi.org/10.1016/j.jfca.2023.105713

Yar, H., Ahmad, Z., Min, F. U., Ullah, W., & Wook, S. (2023). A modified YOLOv5 architecture for efficient fire detection in smart cities. 231(May).

Yar, H., Ullah, W., Khan, Z. A., & Baik, S. W. (2023). An Effective Attention-based CNN Model for Fire Detection in Adverse Weather Conditions. 206(May), 335–346.

Ye?ilmen, S., & Tatar, B. (2022). Efficiency of convolutional neural networks (CNN) based image classification for monitoring construction related activities: A case study on aggregate mining for concrete production. Case Studies in Construction Materials, 17(April). https://doi.org/10.1016/j.cscm.2022.e01372