Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques

Author:

Buriboev Abror Shavkatovich12,Rakhmanov Khoshim3ORCID,Soqiyev Temur4,Choi Andrew Jaeyong1ORCID

Affiliation:

1. School of Computing, Department of AI-Software, Gachon University, Seongnam-si 13306, Republic of Korea

2. Department of Infocommunication Engineering, Tashkent University of Information Technologies, Tashkent 100084, Uzbekistan

3. Department of Digital and Educational Technologies, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 140100, Uzbekistan

4. Digital Technologies and Artificial Intelligence Research Institute, Tashkent 100125, Uzbekistan

Abstract

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings.

Publisher

MDPI AG

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