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Image super-resolution reconstruction method for electric power grid inspection robot

A technology for super-resolution reconstruction and power grid inspection, applied in image data processing, graphic image conversion, instruments, etc., can solve the problems of small occupied area, large occupied area, low resolution, etc. The effect of improving accuracy and good extraction effect

Pending Publication Date: 2022-08-09
周莉莎
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the inspection robot’s running lines are fixed in pairs, resulting in the parts close to the lens occupying a large area in the image with high resolution, while the parts farther away from the lens occupy a small area in the image and have low resolution
However, low-resolution images have poor visual effects. Whether it is automatic recognition or manual recognition, there is a high probability of missed inspections, which weakens the reliability of inspections.

Method used

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  • Image super-resolution reconstruction method for electric power grid inspection robot
  • Image super-resolution reconstruction method for electric power grid inspection robot
  • Image super-resolution reconstruction method for electric power grid inspection robot

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] Before model training, you first need to obtain appropriate training and testing datasets. The data set used in this embodiment is composed of a combination of public data sets and self-built data sets. The training dataset contains a total of 1,300 images, of which 1,000 are from the DIV2K dataset, and the other 300 are high-definition images of power components taken at the inspection site. There are two test data sets, one is the public data set BSD100, which contains 100 images, and the other is the images of power facility components taken at the inspection site, which contains 50 images, which constitute a self-built test set. All images are bicubic downsampled to obtain a low-resolution image corresponding to the high-resolution image, which is used to simulate a natural image1.

[0059] The structure of the image super-resolution reconstruction network in this embodiment is as follows figure 1 As shown, the pre-convolutional layer 2 is an ordinary convolutiona...

Embodiment 2

[0066] In this embodiment, an ablation experiment is performed on the basis of Embodiment 1, and the modulation feature map SK in Embodiment 1 is removed from the CS attention mechanism 33 and input to the IK attention mechanism 34, and other parts of the network remain unchanged. The modified two attention The structure of the force mechanism is as Image 6 shown. Using the same conditional training and testing model as in Example 1, the comparison results are as follows:

[0067]

[0068] From the results in the above table, it can be seen that inputting the modulation feature map SK from the CS attention mechanism 33 to the IK attention mechanism 34 can significantly improve the performance of the model and help improve the super-resolution reconstruction effect.

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Abstract

The invention discloses an image super-resolution reconstruction method for an electric power grid inspection robot. The image super-resolution reconstruction method comprises the steps of training an image super-resolution reconstruction network, inputting a natural image into the network, performing up-sampling and reconstruction, outputting a high-definition electric power part image and the like. The image super-resolution reconstruction network comprises a front convolutional layer, a joint information extraction module, a hierarchical information fusion module and an image output module, a CS attention module and an IK attention module are arranged in the joint information extraction module, a feature map is modulated through an attention mechanism, the enhancement effects of the two attention mechanisms are superposed, and the image super-resolution reconstruction is realized. And the network has a good extraction effect on some fine high-frequency features in the low-resolution image. After super-resolution reconstruction, the visual effect of the image is improved, and the probability of missing detection is reduced.

Description

technical field [0001] The invention belongs to the technical field of electric power and artificial intelligence, and in particular relates to an image super-resolution reconstruction method for a power grid inspection robot. Background technique [0002] In order to maintain the normal operation of the power grid system, it is necessary to periodically check the relevant power equipment and eliminate potential faults in time. Traditional inspection tasks basically rely on manual operations. The inspection effect is easily affected by external factors, and there are great potential safety hazards. Moreover, with the increase in labor prices, the cost is also increasing. In this case, the advantages of inspection robots are becoming more and more obvious, and the scope of application is greatly improved. [0003] The inspection robots are basically equipped with image acquisition equipment, which can obtain images of power grid facilities that need to be inspected by shooti...

Claims

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Application Information

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IPC IPC(8): G06V10/20G06V10/774G06V10/80G06V10/82G06N3/04G06T3/40G06Q50/06G07C1/20
CPCG06V10/20G06V10/774G06V10/806G06V10/82G06T3/4076G06Q50/06G07C1/20G06N3/045Y04S10/50
Inventor 周莉莎胡元晖
Owner 周莉莎