NotiPoy: Early Notification of Detected Fire Using Image Processing Applying Convolutional Neural Network

Abstract

Fire incident is one of the most common undesirable events that cause damages toward houses and human. It can start anytime and anywhere without any prior hint when it would happen. With the cause it brings, the implementation of a device capable of detecting and providing notification is necessary. This study presents the method of early-stage fire-flame detection utilizing image processing integrated with the Android application for emergency notification. The proposed system utilized the image processing applying Convolutional Neural Network (CNN) integrated with mobile app as early notification to the end user. After executing 3999 iterations of 210 images are used for testing to get the Train Accuracy (how accurate the training is) and Cross entropy (how far is the prediction of the actual result), it resulted to 100% trained accuracy and 96% validation. The test evaluation accuracy result was 99% using a cached file of 210, it means that expect the model to perform ~99% accuracy on the new data. From the test that has been done, the proposed system was able to capture the fire flame images and send necessary notification real time. The test results show the captured actual images and the information sent to the end user. The summary test results verified that all gathered data was higher than 90% to be able to send notification. The android app used to show both the pre-investigation information useful for the house owner and BFP personnel.

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