Detection of forest fire using deep convolutional neural networks with transfer learning approach

• Published in: Applied soft computing 2023-08, Vol.143, p.110362, Article 110362
• Main Authors: Reis, Hatice Catal, Turk, Veysel
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Computer vision; Deep learning; Hyper-parameter optimization; Transfer learning; Wildfires detection
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110362

Forest fires caused by natural causes such as climate change, temperature increase, lightning strikes, volcanic activity or human effects are among the world’s most dangerous, deadly, and destructive disasters. Detection, prevention, and extinguishing forest fires is challenging. In addition, forest fires can cause habitat destruction that cannot be controlled in time and cause great material and moral losses. Therefore, fast and accurate Detection of forest fires is vital in emergency response. Here, in solving the problem, the transfer learning method from deep learning sub-topics can be used, which allows the application of pre-trained networks to a new problem. The Fire Luminosity Airborne-based Machine learning Evaluation dataset (consisting of forest fire images) obtained by Unmanned Aerial Vehicle was used in this study. In the Detection of forest fire images in the dataset, InceptionV3, DenseNet121, ResNet50V2, NASNetMobile, VGG-19 deep learning algorithms, transfer learning techniques that can produce more successful results than networks trained from scratch, and hybrid proposed with Support Vector Machine, Random Forest, Bidirectional Long Short-Term Memory, Gated Recurrent Unit algorithms methods have been applied. In the classification study with the Fire Luminosity Airborne-based Machine learning Evaluation dataset in performance measurement, 97.95% accuracy was obtained from the DenseNet121 model, which was started with random weights. In the transfer learning study using ImageNet weights, satisfactory results were obtained with 99.32% accuracy in the DenseNet121 model. We anticipate that working in forest fire detection and response can be entirely satisfactory. [Display omitted] •For forest fire detection, traditional machine learning algorithms SVM, RF, and deep learning algorithms InceptionV3, DenseNet121, ResNet50V2, VGG19, and NASNetMobile are used.•Transfer learning and fine-tuning techniques were applied in the classification study with deep learning architectures.•Optimization techniques have been applied to determine the optimal parameter of machine learning algorithms.•Various hybrid methods have been proposed with machine learning methods. In the proposed hybrid study with InceptionV3+GRU, 99.32% accuracy has been achieved.