A method of clip defect detection based on depth learning and machine vision fusion

A technology of defect detection and deep learning, which is applied in the direction of optical defect/defect, scientific instruments, instruments, etc., can solve the problem of low detection accuracy, achieve the effect of improving detection accuracy, simple realization, and high recognition accuracy

Inactive Publication Date: 2018-12-18
GUILIN UNIV OF ELECTRONIC TECH
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the problem solved by the present invention is to overcome the probl...

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
  • A method of clip defect detection based on depth learning and machine vision fusion
  • A method of clip defect detection based on depth learning and machine vision fusion
  • A method of clip defect detection based on depth learning and machine vision fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The specific embodiments of the present invention are further described below with reference to the accompanying drawings, but the present invention is not limited.

[0034] figure 1 A clip defect detection method based on deep learning and machine vision fusion is shown, including the following steps:

[0035] (1) Image acquisition of the clip workpiece;

[0036] (2) To detect tooth defects on the clips based on the deep learning method;

[0037] (3) Detection of clip size defects based on machine vision;

[0038] (4) Finally, the clip defect detection results are obtained by statistics.

[0039] In step (1), the red bowl lamp is used for illumination, and the dimensional vision MV-VD130 industrial digital camera is used to take pictures and collect, and the image resolution is 1280*1024.

[0040] In step (2), the concrete steps are as follows:

[0041] 1) Establish a clip tooth shape defect data set, which includes flat tooth, rotten tooth, heavy tooth, light pla...

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 clip defect detection method based on depth learning and machine vision fusion. The invention relates to the field of automatic detection technology, which aims at overcomingthe problem of low detection accuracy caused by the manual extraction of features in the clip quality detection method based on machine vision. The method comprises the following steps: (1) image acquisition of the clip workpiece; (2) tooth defect detection of the clip through depth learning; (3) size defect detection based on the method of machine vision; (4) obtaining of the results of defect detection through statistics. The technical scheme of the invention is simple to realize and can effectively improve the detection accuracy rate of the clip defects.

Description

technical field [0001] The invention relates to the technical field of automatic detection, in particular to a clip defect detection method based on deep learning and machine vision fusion. Background technique [0002] With the development of prestressed anchorage technology, anchorage clips (referred to as clips) account for an increasing proportion of prestressed anchorage engineering, and are widely used in the construction of various bridges and roads. Damage directly affects the safety of prestressed structures. If workpieces with quality defects flow into the market, it may lead to safety accidents in buildings in severe cases, which will greatly damage social security and people's property, and also bring great economic losses to production enterprises. Liability Risk. [0003] Therefore, the detection of clip quality defects is a very important link. At present, manual detection is mostly used in the production line, which has low efficiency and high cost. In view...

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): G06T7/00G06N3/04G01N21/88
CPCG06T7/0004G01N21/8851G01N2021/8887G06N3/045
Inventor 王健唐滔曾庆宁
Owner GUILIN UNIV OF ELECTRONIC TECH
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