Aerial power component image classification method based on knowledge transfer learning

Abstract

The invention relates to the field of combination of deep learning and machine vision in artificial intelligence, in particular to a knowledge transfer learning-based aerial power part image classification method, which comprises the following steps of establishing a convolutional neural network GoogLeNet; adjusting and optimizing the convolutional neural network GoogLeNet, on the basis of the convolutional neural network GoogLeNet, replacing the last three layers of the convolutional neural network GoogLeNet with a full connection layer, a softmax layer and a classification output layer, andcarrying out optimization setting; when the network is trained, obtaining the network parameters by combining multiple simulation experiments and a Bayesian optimization algorithm; performing normalization preprocessing on the acquired electric power part images, inputting the images into the set new deep convolutional neural network obtained in the step 2 for learning, and performing classification according to the types of insulators, hardware fittings, towers and the like; and verifying by performing simulation experiments.

Description

technical field [0001] The invention relates to the field of combining deep learning and machine vision in artificial intelligence, which is applied to the classification and identification of power components in the inspection process of transmission lines in power systems, thereby ensuring the safe operation of transmission lines. Background technique [0002] The transmission line is a vital part of the power grid system. As the backbone line of the power grid system, it plays a decisive role in the reliability, long-term, safe and stable operation of the entire power grid, and the long-term effective operation of the power grid system is directly related to healthy development of the national economy. With the implementation of the transmission network construction project, the number of transmission lines has increased sharply, the workload of line inspections has increased sharply, the geographical environment is complex, and the weather conditions are changing, making...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: how to solve the problem that it is difficult to realize accurate classification and precise positioning of power components due to the complex background of the aerial power component image, the uncertain shooting angle, and the huge amount of data

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