Power supply security image management based on block discrete cosine transform perceptual Hash algorithm and ResNet model

Abstract

In view of the low efficiency of manual processing of repeated power supply security control image, based on the perceptual Hash algorithm (PHA), PHA based on block discrete cosine transform hash (BDCT) was proposed, abbreviated as BDCT-PHA. BDCT-PHA was used for image duplication removal. The algorithm could process the JPEG compressed image, and had high duplication removal accuracy and low misjudgment rate. Then, the ResNet network structure was improved, and the convolutional neural network (CNN) was selected for image classification. Firstly, the image was transformed into the format of VOC data set, and then trained. This method prevented the gradient disappearance when the network model was deepened, and improved the classification efficiency while reducing the amount of calculation. Simulation results show that the proposed method can accurately identify duplicate images and indicate the number of similar images. When classifying power supply safety management and control images, the loss function value can converge to 4.785% and the classification accuracy is as high as 94.46%.

Keywords

• block discrete cosine transform
• perceptual hash algorithm
• convolutional neural network
• power supply security control images
• image duplication removal
• image classification
• resnet model