L2 regularized deep convolutional neural networks for fire detection

Autor: Sanjiban Sekhar Roy, Vatsal Goti, Aditya Sood, Harsh Roy, Tania Gavrila, Dan Floroian, Nicolae Paraschiv, Behnam Mohammadi-Ivatloo
Rok vydání: 2022
Předmět:
Zdroj: Journal of Intelligent & Fuzzy Systems. 43:1799-1810
ISSN: 1875-8967
1064-1246
Popis: Fire calamity is one of the worst adversarial events that can happen to the human race. Fire disaster can happen as a manmade disaster or even naturally, and it may cause environmental, social, and financial damages as well. In order to minimalize the unwanted fire calamity, early detection of fire eruptions coupled with immediate and effective response is extremely vital to disaster management systems. The classification of forest fire and non fire images using deep learning techniques has recently received popularity. Detection and prevention of forest fire have lot of significance from the perspective of the forest fire department, specially for the fire and arson investigators. There are shortcomings in the current mechanisms of forest fire detection in terms of accuracy. Hence, we propose a fire detection model using LeNet5 convolutional neural networks (CNN), which can spot fire in outdoor environments by classifying fire and non fire images. L2 regularization is critical technique that manipulates the complexity of the convolutional neural network model. In our work fire images have certain features that decide if the image is fire or non fire.A weight is assigned to every feature. Regularization used to help to reduce the over fitting that used to caused by plenty of weights. Our proposed provides the directiontowards developing a system that detects the early stages of forest fire.This model can further be utilized to prevent the damage caused by the fire. A CNN is a deep learning method, which has been adopted in order to detect the images of fire and non-fire. With the non sparse solution of L2 regularization we have obtained around 87% of train accuracy, 71% of validation accuracy and 70% of test accuracy after running 10 epochs.
Databáze: OpenAIRE
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