Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Probability graph visualization method and device for defect detection

A defect detection and probability map technology, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of not being able to visually display the probability information of pixel defect categories, missing background category related information, etc.

Pending Publication Date: 2021-11-16
BEIJING LUSTER LIGHTTECH +1
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a probability map visualization method and device for defect detection to solve the problems in the prior art that the probability information of the pixel defect category cannot be intuitively displayed, and the related information of the background category is missing

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
  • Probability graph visualization method and device for defect detection
  • Probability graph visualization method and device for defect detection
  • Probability graph visualization method and device for defect detection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] see figure 2 , a schematic flowchart of a probability map visualization method for defect detection provided in an embodiment of the present application. The method comprises the steps of:

[0045] In step 201, the target image to be detected is input into the semantic segmentation model to obtain a multi-channel probability map corresponding to the target image to be detected, wherein the channels in the multi-channel probability map correspond to categories one by one, and the category is Split result categories.

[0046]In some embodiments, the semantic segmentation model is set to classify each pixel in the target image to be detected, perform image segmentation on the pixel of the defect area on the surface of the object, and the output result is a multi-channel probability map of C*H*W, wherein, C is the number of categories, H is the width of the image, and W is the height of the image, and the categories correspond to the channels in the multi-channel probabi...

Embodiment 2

[0074] Figure 8 A schematic structural diagram of the grayscale image provided by Embodiment 2 of the present application is shown.

[0075] The probability threshold is set according to the multi-channel probability map, and the detection results with low confidence are filtered out. In some embodiments, such as Figure 8 As shown, a defect is detected in the upper left corner, but the confidence of the defect is low, and the gray value is about 157. By calculating the corresponding probability (157 divided by 255 is approximately equal to 0.6), set the probability threshold to 0.6 to filter out the defect. defects.

[0076] Figure 9 A schematic structural diagram of the pseudo-color map provided in Embodiment 2 of the present application is shown.

[0077] The grayscale image is color-mapped by the color-mapping algorithm to obtain the corresponding pseudo-color image, such as Figure 9 As shown, there is an area close to the background (blue) near the right side of t...

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 relates to the technical field of defect detection, and relates to a probability graph visualization method and device for defect detection. The problems that in the prior art, probability information of pixel point defect categories cannot be visually displayed, and background category related information is omitted are solved. The method comprises the following steps: inputting a to-be-detected target image into a semantic segmentation model to obtain a multi-channel probability graph corresponding to the to-be-detected target image; according to the multi-channel probability graph, obtaining the probability of the same pixel point in each category, and comparing to obtain the maximum probability of each pixel point; based on the category corresponding to each maximum probability, recording an index number of the category corresponding to each maximum probability; according to an index number corresponding to a preset category, judging a category corresponding to each maximum probability; according to the category, processing each maximum probability to obtain a single-channel probability graph containing the probability that each pixel point belongs to a certain category; and converting the single-channel probability graph into an RGB image to obtain a pseudo-color graph so as to realize visualization of the probability graph.

Description

technical field [0001] The present application relates to the technical field of defect detection, and in particular to a method and device for visualizing a probability map for defect detection. Background technique [0002] Surface defect detection uses advanced machine vision detection technology to detect spots, pits, scratches, color differences, defects and other defects on the surface of the workpiece. In terms of surface defect detection, semantic segmentation can accurately predict the contour of defects. Therefore, for defect detection tasks with high precision requirements, the semantic segmentation model is mainly used for surface defect detection. [0003] At present, the semantic segmentation model used to classify each pixel in the image is used to detect surface defects. Usually, the target image containing defects is output through the semantic segmentation model and corresponds to the target image to be detected. Each pixel belongs to A multi-channel proba...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/34G06K9/62
CPCG06F18/213G06F18/2415Y02P90/30
Inventor 胡凯姚毅杨艺全煜鸣金刚彭斌
Owner BEIJING LUSTER LIGHTTECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com