E-FFireNet: Efficient Transfer Learning to Forest Fire Classification

Quispe S.E.P.; Zucso L.B.H.; Calla L.G.M.; Hilasaca L.M.H.; Belizario I.V. · Proceedings - 2023 4th International Conference on Information Systems and Software Technologies, ICI2ST 2023 (2023) · DOI: 10.1109/ici2st62251.2023.00015

Resumen

The importance of the classification of forest fires through computational methods lies in their applicability for early detection and timely warning to prevent their spread. According to the state of the art, the classification of forest fires was approached by proposals based on machine learning (ML) and deep learning (DL), more specifically on Convolutional Neural Networks (CNN). In practice, DL-based methods prove to be superior to traditional ML methods. Recently, FFireNet, a CNN model that presents optimal results for classifying images of forest fires, was proposed. On this basis, in this work, we present a methodology to create CNN models by transfer learning applied specifically to the classification of forest fires, where techniques such as data augmentation, transfer learning, transfer learning and fine tuning were included. The proposal allowed us to define E-FFireNet, an efficient model with superior results than FFireNet. The experiments showed classification results of 99,73 (accuracy), 99,48 (precision), 100,0 (recall) and 0,263 (error).