Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Fire Detection Based on Image Learning by Collaborating CNN-SVM with Enhanced Recall

  • Yongtae Do (Major of Electrical Engineering, Daegu University)
  • Received : 2024.04.25
  • Accepted : 2024.05.17
  • Published : 2024.05.31
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Abstract

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

Keywords

Acknowledgement

This research was supported by the Daegu University Research Grant, 2019.

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