Publication Date: 2024/02/09
Abstract: In Nigeria, over 2,000 fire outbreaks reported resulted in ₦1 trillion worth of property damages. Likewise, a record of 31 market fires was reported in various states in the country. These numbers point to the dangers posed by fires and the need of a faster detection approach. This paper presents a computer vision-based smoke and fire detection system that is run on an edge server. The proposed system consists of a custom convolutional neural network (CNN) model which is utilized to extract features in image frames to identify fire and smoke occurrences. The k-fold cross validation algorithm has been proved on a simplified CNN model which has a small number of layers in order to improve the performance of the image classification. The experimental analysis of the model shows that the proposed system is capable of classifying fire images accordingly with an ROC value of over 0.67 in each class. This model is recommended for use in deep learning tasks that require automatic feature extraction and object detection in image processing applications.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.10639122
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JAN1596.pdf
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