Learn a Deep Convolutional Neural Network for Image Smoke Detection

Smoke detection is the key to industrial safety warnings and fire prevention, such as flare smoke detection in chemical plants and forest fire warning. Due to the complex changes in smoke color, texture and shape, it is difficult to identify the smoke in the image. Recently, more and more scholars have paid attention to the research of smoke detection. In order to solve the above problems, we propose a convolutional neural network structure designed for smoke characteristics. The characteristics of smoke are only complicated in simple features, and no deep semantic structure information needs to be extracted. Therefore, there is no performance improvement in deepening the depth of the network. We use a 10-layer convolutional neural network to hop the features of the first layer of convolution extraction to the back layer to increase the network's ability to extract simple features. The experimental results show that our convolutional neural network model has fewer parameters than the existing deep learning method, and the accuracy rate in the smoke database is optimal.