Volume 21, Issue 4 (December 2025)                   IJEEE 2025, 21(4): 3601-3601 | Back to browse issues page

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Sistani T, Kazemitabar S J. Deep Learning Approach for Forest Fire Detection: A CNN Classification Model on the DeepFire Dataset. IJEEE 2025; 21 (4) :3601-3601
URL: http://ijeee.iust.ac.ir/article-1-3601-en.html
Deep Learning Approach for Forest Fire Detection: A CNN Classification Model on the DeepFire Dataset
Tara Sistani , Seyed Javad Kazemitabar
Abstract:   (148 Views)
Forests play several vital roles in our lives and provide various resources. However, in recent years, the increasing frequency of wildfires has led to the widespread burning and destruction of many forests and wildlands. Therefore, detecting forest fires and finding suitable solutions to address this issue has become one of the critical challenges for researchers. Today, with the advancement of artificial intelligence, forest fire detection using deep learning is an important method with the aim of increasing the efficiency of forest fire detection and monitoring systems. In this article, a method based on a type of convolutional neural network called Xception is proposed for classifying forest fire images. In this method, transfer learning technique is used on the proposed neural network and a new classifier is designed for the problem. Also, various hyperparameters have been used to optimize the performance of the proposed model. The proposed method is performed on the DeepFire dataset, which contains 1900 images equally divided between fire and no-fire classes. The results obtained from the implementation of the proposed method show that this method with an accuracy of 99.47% has achieved a favorable performance in classifying forest fire images.
Keywords: Artificial Intelligence, Classification, Convolutional Neural Network, Deep Learning, Forest Fire Detection.
Full-Text [PDF 1935 kb]   (37 Downloads)    
Type of Study: Research Paper | Subject: Image Processing
Received: 2024/12/09 | Revised: 2025/07/17 | Accepted: 2025/06/26
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.