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Deep CNN-ELM Hybrid Models for Fire Detection in Images

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In this paper, we propose a hybrid model consisting of a Deep Convolutional feature extractor followed by a fast and accurate classifier, the Extreme Learning Machine, for the purpose of fire detection in images. The reason behind using such a model is that Deep CNNs used for image classification take a very long time to train. Even with pre-trained models, the fully connected layers need to be trained with backpropagation, which can be very slow. In contrast, we propose to employ the Extreme Learning Machine (ELM) as the final classifier trained on pre-trained Deep CNN feature extractor. We apply this hybrid model on the problem of fire detection in images. We use state of the art Deep CNNs: VGG16 and Resnet50 and replace the softmax classifier with the ELM classifier. For both the VGG16 and Resnet50, the number of fully connected layers is also reduced. Especially in VGG16, which has 3 fully connected layers of 4096 neurons each followed by a softmax classifier, we replace two of these with an ELM classifier. The difference in convergence rate between fine-tuning the fully connected layers of pre-trained models and training an ELM classifier are enormous, around 20x to 51x speed-up. Also, we show that using an ELM classifier increases the accuracy of the system by 2.8% to 7.1% depending on the CNN feature extractor. We also compare our hybrid architecture with another hybrid architecture, i.e. the CNN-SVM model. Using SVM as the classifier does improve accuracy compared to state-of-the-art deep CNNs. But our Deep CNN-ELM model is able to outperform the Deep CNN-SVM models. (Preliminary version of some of the results of this paper appear in “Deep Convolutional Neural Networks for Fire Detection in Images”, Springer Proceedings Engineering Applications of Neural Networks 2017 (EANN’17), Athens, Greece, 25–27 August).