| Summary: | It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them are based on hand-crafted features. To improve the performance of smoke detection, we propose a novel deep normalization and convolutional neural network (DNCNN) with 14 layers to implement automatic feature extraction and classification. In DNCNN, traditional convolutional layers are replaced with normalization and convolutional layers to accelerate the training process and boost the performance of smoke detection. To reduce overfitting caused by imbalanced and insufficient training samples, we generate more training samples from original training data sets by using a variety of data enhancement techniques. Experimental results show that our method achieved very low false alarm rates below 0.60% with detection rates above 96.37% on our smoke data sets.
|
|---|
