Photovoltaic cell panel grid line defect detection method and system based on artificial intelligence

A photovoltaic panel and defect detection technology, applied in the field of artificial intelligence, to achieve accurate detection results, improve detection efficiency, and realize the effect of automation

Inactive Publication Date: 2021-01-12
郭燕
View PDF0 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the above-mentioned technical problems, the present invention provides a method and system for detec

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Photovoltaic cell panel grid line defect detection method and system based on artificial intelligence
  • Photovoltaic cell panel grid line defect detection method and system based on artificial intelligence
  • Photovoltaic cell panel grid line defect detection method and system based on artificial intelligence

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] In order to further explain the technical means and effects of the present invention to achieve the intended purpose of the invention, the following is combined with the accompanying drawings and preferred embodiments, and a method for detecting defects in photovoltaic cell grid lines based on artificial intelligence proposed according to the present invention and The system, its specific implementation, structure, features and functions, are described in detail as follows.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention.

[0044] It should be noted that the ideal image of a photovoltaic panel is:

[0045] 1. The grid line is a vertical thin line that appears periodically;

[0046] 2. The area between the grid lines is a solid color or monochrome area;

[0047] 3. The ideal image of a photovoltaic panel only includes grid line...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a photovoltaic cell panel grid line defect detection method and system based on artificial intelligence, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring an image of a single photovoltaic cell panel; constructing an image matrix according to prior color features in the photovoltaic cell panel image, and performing sliding window calculation on the image matrix to obtain a single-channel image; obtaining a compression value of each column of pixels of the single-channel image as a one-dimensional sequence representing the photovoltaic cell panel image; and inputting the one-dimensional sequence into a time convolution network to obtain the periodicity of the grid line in the photovoltaic cell panel image, and judging the defect position of the grid line according to the abnormal point. According to the invention, the semantic segmentation network type is adopted to identify the content of each column in the image, the image processing mode does not need to debug parameters, the periodicity of the content of the photovoltaic cell panel is utilized to adapt to cell panel images of various sizes, the preprocessing process is simplified, and the generalization ability is high. According to the invention, the influence of camera parameters and illumination intensity can be eliminated, automation is realized, and the detection efficiency and accuracy are improved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an artificial intelligence-based detection method and system for photovoltaic cell grid line defects. Background technique [0002] Solar energy, as a renewable clean energy, has been used more and more in society. As the carrier of solar energy, photovoltaic panels are usually expected to have a long service life and high conversion rate, and in the manufacturing process Gridline defects appearing in , limit its applicability. Therefore, effective detection methods are very important to improve the quality of photovoltaic panels. [0003] Raster line detection usually uses the background modeling method and the frame difference method, but often requires a standard template. At the same time, the image processing method needs to artificially set more empirical thresholds, which makes the generalization ability of the system poor. The difference in strength will invalidat...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/00G06T7/194G01N21/88G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06T7/194G01N21/8851G06N3/08G06T2207/20132G06T2207/20081G06T2207/20084G06T2207/10024G06N3/045G06F18/24
Inventor 郭燕余波
Owner 郭燕
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap