Improved full-convolutional neural network-based power transmission line insulator state recognition method

Abstract

The present invention discloses an improved full-convolutional neural network-based power transmission line insulator state recognition method. The method includes the following steps that: S1, the picture of a power transmission line insulator is collected through an unmanned aerial vehicle; S2, classification regression and position regression are performed on the image through a target detection network Faster R-CNN so as to intercept a separate insulator picture; S3, semantic segmentation is performed on the insulator picture through using a full-convolutional neural network; S4, fine segmentation is performed through a full-connection condition random field; S5, noise points in the image are filtered by using a morphological operation method; and S6, the insulator is classified through a deep learning classification network, and the status of the insulator is determined. According to the method of the invention, training and parameter adjustment and optimization are performed on labeled insulator pictures; the status of the power transmission line insulator can be effectively identified; the subjective influence of manual setting of thresholds and the randomness of manual extraction of features in traditional insulator status recognition can be avoided; the efficiency of line inspection can be significantly improved; and the difficulty of the line inspection can be decreased.

Description

technical field [0001] The invention belongs to the fields of deep learning image processing and electric defect recognition, and in particular relates to a method for state recognition of transmission line insulators based on an improved fully convolutional neural network. Background technique [0002] Insulators in high-voltage transmission lines are very important components in transmission lines. However, insulators are exposed to the wild for many years and are subject to damage from wind, snow, rain, fog and human factors. The safety of the power grid may cause incalculable losses. However, most of the power transmission lines are in the traffic dead zone, no man's land, which makes the inspection difficult and the inspection cycle is long. With the increasing scale of high-voltage transmission lines, manual line inspection will also be affected by factors such as weather and terrain, and the task of inspecting insulator faults is becoming increasingly heavy. [0003...

AI Technical Summary

Problems solved by technology

This method has many disadvantages. First, the color space is easily affected by illumination. Second, the maximum between-class variance method needs to manually set th

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