Convolutional Neural Network (CNN-SA) based Selective Amplification Model to Enhance Image Quality for Efficient Fire Detection

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Sagnik Sarkar 1,* Aditya Sunil Menon 1 Gopalakrishnan T 2 Anil Kumar Kakelli 2

1. Computer Science and Engineering, VIT University, Vellore 632014, India

2. School of Computer Science and Engineering (SCOPE), VIT University, Vellore 632014, India

* Corresponding author.


Received: 24 Aug. 2021 / Revised: 7 Sep. 2021 / Accepted: 18 Sep. 2021 / Published: 8 Oct. 2021

Index Terms

Selective Amplification, Convolutional Neural Network, Image Processing, Real-time Fire Detection, Image enhancement, Pattern Recognition.


Fires spread quickly and are extremely difficult to contain, and cause a great deal of damage to people and property. Current domestic systems for detecting outbreaks of fire, such as smoke detectors, are prone to reliability issues and will benefit greatly from having a secondary system in place to confirm the presence of a fire in the premises. In this paper, we have proposed a novel image pre-processing algorithm known as the Selective Amplification. This technique enhances images that are to be used in Convolutional Neural Networks, which are then trained on pre-processed images to detect fires with high accuracy. The efficacy of the proposed technique is verified by training two identical Convolutional Neural Network models on the same dataset of images. We train the proposed model on a version of the dataset that uses Selective Amplification for data pre-processing. The proposed model then demonstrates an improvement in the accuracy of the detection of fire in real-time over by 12.85%, compared to an identical model trained on the dataset without any pre-processing performed beforehand.

Cite This Paper

Sagnik Sarkar, Aditya Sunil Menon, Gopalakrishnan T, Anil Kumar Kakelli, " Convolutional Neural Network (CNN-SA) based Selective Amplification Model to Enhance Image Quality for Efficient Fire Detection", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.13, No.5, pp. 51-59, 2021. DOI: 10.5815/ijigsp.2021.05.05


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