High-voltage line insulator defect detection method based on generative adversarial network

Abstract

The invention relates to a high-voltage line insulator defect detection method based on a generative adversarial network. The method comprises the following steps: establishing an image data set; constructing an improved GANomaly model, wherein the improved GANomaly model sends a feature map output by each convolution layer in the encoder sub-network to the decoder sub-network for feature fusion;constructing a loss function of the improved GANomaly model; training an improved GANomaly model; sending the defective insulator image in the test set into a trained improved GANomaly model to obtainan adaptive threshold; sending a to-be-detected insulator image to the trained improved GANomaly model to judge the image type. Compared with the prior art, the method solves the problems that the number of negative samples in the insulator image is small, the negative samples are difficult to collect, a large amount of energy is needed to label data, and the feasibility and adaptability of the training process are improved.

Description

technical field [0001] The invention relates to the field of insulator defect detection, in particular to a high-voltage line insulator defect detection method based on a generative confrontation network. Background technique [0002] Insulators are important devices for electrical isolation and mechanical fixing of wires in high-voltage power transmission systems. Insulator failure directly threatens the stability and safety of transmission lines. Statistically, accidents caused by insulator defects account for the highest proportion of power system failures. Therefore, intelligent and timely detection of insulator defects is particularly important. In recent years, with the emergence of aerial work platforms such as helicopters and drones, their work is efficient, accurate, and safe, and has become an important tool for electrical equipment inspection. In order to overcome the limitations of manual inspection, it has become urgent to develop automated defect inspection t...

AI Technical Summary

Problems solved by technology

However, this method needs to use a large number of insulator image data with defects. In practice, the number of defect samples is small and difficult to obtain. Moreover, due to the simple operation, the results are easily affected by complex backgrounds such as towers, mountains, and rivers in real samples. There is no comparison. high robustness

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