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Published in:
2017 | OriginalPaper | Chapter
A Novel Fire Detection Approach Based on CNN-SVM Using Tensorflow
Authors : Zhicheng Wang, Zhiheng Wang, Hongwei Zhang, Xiaopeng Guo
Published in: Intelligent Computing Methodologies
Publisher: Springer International Publishing
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Abstract
In this paper, we propose a novel approach to detect fire based on convolutional neural networks (CNN) and support vector machine (SVM) using tensorflow. First of all, we construct a large number of different kinds of fire and non-fire images as the positive and negative sample set. Next we apply Haar feature and AdaBoost cascade classifier to extract the region of interest (ROI). Then, we use CNN-SVM to filter the results of Haar detection and reduce the number of negative ROI. The CNN is constructed to train the dataset with four convolutional layers. Finally, we utilize SVM to replace the fully connected layer and softmax to classify the sample set based on the training model in order. Experimental results show that the method we proposed is better than other methods of fire detection such as CNN or SVM etc.