TY - EJOUR
AU - Almoussawi, Zainab Abed
AU - Khalid, Raed
AU - Obaid, Zahraa Salam
AU - Mashhadani, Zuhair I. Al
AU - Al-Majdi, Kadhum
AU - Alsaddon, Refad E.
AU - Abed, Hassan Mohammed
PY - 2024
DA - February
TI - Fire Detection and Verification using Convolutional Neural Networks, Masked Autoencoder and Transfer Learning
T2 - Majlesi Journal of Electrical Engineering
VL - 16
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/fire-detection-and-verification-using-convolutional-neural-networks-masked-autoencoder-and-transfer-learning/
DO - 10.30486/mjee.2023.1977460.1055
N2 - Wildfire detection is a time-critical application since it can be challenging to identify the source of ignition in a short amount of time, which frequently causes the intensity of fire incidents to increase. The development of precise early-warning applications has sparked significant interest in expert systems research due to this issue, and recent advances in deep learning for challenging visual interpretation tasks have created new study avenues. In recent years, the power of deep learning-based models sparked the researcher’s interests from a variety of fields. Specially, Convolutional Neural Networks (CNN) have become the most suited approach for computer vision tasks. As a result, in this paper we propose a CNN-based pipeline for classifying and verifying fire-related images. Our approach consists of two models, first of which classifies the input data and then the second model verifies the decision made by the first one by learning more robust representations obtained from a large masked auto encoder-based model. The verification step boosts the performance of the classifier with respect to false positives and false negatives. Based on extensive experiments, our approach proves to improve previous state-of-the-art algorithms by 3 to 4% in terms of accuracy.
IS - 4
PB - OICC Press
KW - Fire Detection, masked auto encoder, vision transformers, Convolutional neural networks, transfer learning 
EN -