Method, device and storage medium for non-destructive semantic segmentation of high-resolution images of concrete cracks

A semantic segmentation and concrete technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as excessive calculation, unbalanced categories, and reduced ability of the model to recognize background information

Active Publication Date: 2021-09-14
STATE GRID HUNAN ELECTRIC POWER +2
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the large amount of background information during model training, if all local images are used for semantic segmentation, it will lead to extreme category imbalance and excessive calculation
If only partial images with cracks are selected for semantic segmentation, a large amount of background information will be ignored, thereby reducing the ability of the model to recognize background information. For images with a large amount of background information, more false cracks will inevitably appear Phenomenon

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
  • Method, device and storage medium for non-destructive semantic segmentation of high-resolution images of concrete cracks
  • Method, device and storage medium for non-destructive semantic segmentation of high-resolution images of concrete cracks
  • Method, device and storage medium for non-destructive semantic segmentation of high-resolution images of concrete cracks

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] Such as figure 1 As shown, this embodiment provides a method for lossless semantic segmentation of concrete crack high-score images, including:

[0049] S01: Obtain a high-resolution image of the concrete surface;

[0050] S02: Using a sliding window to intercept the high-resolution image of the concrete surface into several partial images;

[0051] S03: Input several partial images into the pre-trained concrete crack initial identification model one by one, and screen out all the identified local images with crack probability greater than the preset threshold; wherein, the concrete crack initial identification model is based on historical concrete crack height Obtained by training the traditional convolutional neural network with image data;

[0052] S04: Input the selected partial images into the pre-trained concrete crack semantic segmentation model one by one, and output the corresponding pixel-by-pixel classification concrete crack semantic segmentation map, so a...

Embodiment 2

[0069] Such as figure 2 As shown, this embodiment provides a device for lossless semantic segmentation of high-score concrete crack images, including:

[0070] Image acquisition module 1, used to acquire high-score images of concrete surfaces;

[0071] The preprocessing module 2 is used to intercept the high-score image of the concrete surface into several partial images by adopting a sliding window;

[0072] The initial identification module 3 is used to input several local images into the pre-trained concrete crack initial identification model one by one, and screen out the identified partial images whose crack probability is greater than the preset threshold;

[0073] The semantic segmentation module 4 is used to input the selected local images one by one into the pre-trained concrete crack semantic segmentation model, and output the corresponding pixel-by-pixel semantic segmentation map of concrete cracks, so as to realize the lossless semantic segmentation of high-scori...

Embodiment 3

[0077] This embodiment provides a computer-readable storage medium, which stores a computer program, and when the computer program is loaded by a processor, executes the method for non-destructive semantic segmentation of a concrete crack high-score image as described in Embodiment 1.

[0078] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

[0079] The present application is described with reference to flowcharts and / or block ...

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 discloses a method, device and storage medium for non-destructive semantic segmentation of high-resolution images of concrete cracks, wherein the method includes: obtaining high-resolution images of concrete surfaces; using sliding windows to intercept high-resolution images of concrete surfaces into several partial images; Input the pre-trained concrete crack identification model one by one, and screen out the local images with crack probability greater than the preset threshold value; input the screened local images into the pre-trained concrete crack semantic segmentation model one by one, corresponding to Output the semantic segmentation map of concrete cracks classified pixel by pixel, and realize the lossless semantic segmentation of high-resolution images of concrete surfaces. This scheme can non-destructively identify the crack pixels in the original image, thereby ensuring the pixel recognition accuracy of the crack; it can effectively alleviate the category imbalance, excessive calculation, too many false cracks and the presence of false cracks in the original high-scoring image caused by other semantic segmentation models. Problems such as messy distribution.

Description

technical field [0001] The invention relates to the field of concrete safety monitoring, in particular to a method, device and storage medium for non-destructive semantic segmentation of concrete crack high-score images. Background technique [0002] In many cases, it is necessary to monitor and detect the safety status of concrete. Among them, patrol inspection is an indispensable part of concrete safety monitoring. Through patrol inspection, cracks and other defects that affect structural safety can be found in time. However, patrol inspection currently mainly relies on manual work, which inevitably has problems such as limited inspection space, time-consuming and labor-intensive inspection, and difficulty in controlling crack details and development process. [0003] In recent years, deep learning technology represented by Convolutional Neural Networks (CNN) has continuously surpassed previous image processing technologies in image classification tasks, and is gradually b...

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): G06T7/00G06K9/34G06K9/62G06N3/04
CPCG06T7/0004G06T2207/20081G06T2207/30132G06V10/267G06N3/045G06F18/24G06F18/214
Inventor 张军朱光明李文军刘珊彭琳峰曾惠芳
Owner STATE GRID HUNAN ELECTRIC POWER
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