TeutongNet: A Fine-Tuned Deep Learning Model for Improved Forest Fire Detection
Ghazi Mauer Idroes, Aga Maulana, Rivansyah Suhendra, Andi Lala, Taufiq Karma, Fitranto Kusumo, Yuni Tri Hewindati, Teuku Rizky Noviandy
Abstract
Forest fires have emerged as a significant threat to the environment, wildlife, and human lives, necessitating the development of effective early detection systems for firefighting and mitigation efforts. In this study, we introduce TeutongNet, a modified ResNet50V2 model designed to detect forest fires accurately. The model is trained on a curated dataset and evaluated using various metrics. Results show that TeutongNet achieves high accuracy (98.68%) with low false positive and false negative rates. The model's performance is further supported by the ROC curve analysis, which indicates a high degree of accuracy in classifying fire and non-fire images. TeutongNet demonstrates its effectiveness in reliable forest fire detection, providing valuable insights for improved fire management strategies.