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.
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Acknowledgments
The authors would like to express their gratitude to the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. This work is supported by the National Science and Technology Support Project of China under Grant 2015BAF04B01 and Science and Technology Innovation Project of Shanghai under Grant 16111107602.
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Wang, Z., Wang, Z., Zhang, H., Guo, X. (2017). A Novel Fire Detection Approach Based on CNN-SVM Using Tensorflow. In: Huang, DS., Hussain, A., Han, K., Gromiha, M. (eds) Intelligent Computing Methodologies. ICIC 2017. Lecture Notes in Computer Science(), vol 10363. Springer, Cham. https://doi.org/10.1007/978-3-319-63315-2_60
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DOI: https://doi.org/10.1007/978-3-319-63315-2_60
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