Power equipment classification method based on deep learning under small sample

• Main Authors: YAO XIN, GUO ZHIBO, CUI ZHENGDA
• Format: Patent
• Language: Chinese; English
• Published: 24.09.2019
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a power equipment classification method based on deep learning under a small sample in the field of image processing. The power equipment classification method comprises the following steps: step 1, obtaining a standardized power equipment infrared image through a substation equipment detection device; step 2, establishing an electric power equipment infrared image sample library, and making a training set, a verification set and a test set; step 3, establishing a small sample learning network, training the established convolutional neural network by using a training set of a sample library, verifying the model through a verification set, and obtaining a connection weight and a bias parameter of the network model after training; and step 4, classifying the infrared images in the test set by using the trained network model to generate a classification result of the infrared images of the power equipment, obtaining a good effect under the condition that the sample size is relatively smal