A high-voltage wire defect detection method based on a regional convolutional neural network

Abstract

The invention discloses a high-voltage wire defect detection method based on a regional convolutional neural network. The method comprises the steps of obtaining a high-voltage wire image defect sample set; inputting the high-voltage wire image into a convolutional neural network, and extracting defect features in the high-voltage wire image to obtain a feature map; inputting the feature map intoan area proposal network additionally provided with a full connection layer, generating a proposal area by the area proposal network, and judging the proposal area; inputting the proposal area and thefeature map which belong to the foreground into a full connection layer for defect classification; correcting and processing the boundary of the proposal area, and optimizing the boundary to obtain amore accurate proposal area; and repeating the steps to iterate the regional proposal network until the training is completed, and obtaining the high-voltage wire defect detection model with the trained parameters to carry out actual defect detection. According to the invention, the Faster R-CNN algorithm is used to identify and position the wire defects, and the method has the advantages of highidentification speed, extremely high accuracy and good robustness.

Description

technical field [0001] The invention relates to the technical fields of machine vision and deep learning, in particular to a high-voltage wire defect detection method based on a region-based convolutional neural network. Background technique [0002] The stable operation of high-voltage transmission lines is an important link to ensure the country's economic development. With the development and popularization of artificial intelligence, intelligent inspection methods are gradually replacing manual inspections of transmission lines, such as drone inspections and robot inspections. The inspection equipment transmits the captured inspection pictures to the ground station through image transmission for inspection personnel to observe, but the massive images generated make the inspection personnel dizzy, so image processing is used to detect defects in high-voltage wires. [0003] There are mainly three types of defects in high-voltage wires: broken strands, corrosion and forei...

AI Technical Summary

Problems solved by technology

The traditional power component recognition process often uses classic machine learning algorithms, such as support vector machine (SVM), random forest or adaboost, combined with shallow features such as gradient, color or texture to detect power components, but the existing classical machine The efficiency and accuracy of the learning algorithm for wire defect recognition are low, and it is difficult to make full use of the information of the inspection image

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