Image Classification Based on Deep Learning for Big Data of Power Grid
There are varieties of sensors and detection equipments in the power grid, generating large amount of data including pictures and videos captured by cameras and unmanned hovers. Automatic classifier is required to process massive number of images before accurate assessment of equipment status is made. As deep learning based classifier has been used as a new effective tool in artificial intelligence domain recently, we construct an image classifier framework based on deep learning workflow on top of a deep learning engine CAFFE (convolutional architecture for fast feature embedding) to handle big data from power grid equipments. However, we identify a major drawback of CAFFE in our implementation that when the input training images stay in the same category in a continuous period, CAFFE's model is invalid regardless of the size of the training set. To bridge the gap, we introduce a preprocessing mechanism into deep learning based classifier. This mechanism improves the adaptability of classifier to images of arbitrary size and sequence, and reduces the time to convergence of training the classifier. Experimental results show that our framework is able to classify images of different devices more accurately with much less training time.