A yarn hairiness defect detection method based on image processing

A technology of image processing and defect detection, which is applied in the field of yarn hairiness, can solve problems such as the mixing of defective products, low accuracy, complicated operation, etc., and achieve the effects of cost saving, easy operation and high stability

Inactive Publication Date: 2019-05-03
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0003] In order to solve the problem that existing yarn hairiness is checked manually and with specific tools during defect detection, it is easy to have defective products mixed in, resulting in low accuracy and complicated operation; the purpose of the present invention is to provide a method based on image processing Yarn Hairiness Defect Detection Method

Method used

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Experimental program
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Embodiment Construction

[0016] The specific embodiment adopts the following technical scheme: a kind of yarn hairiness defect detection method based on image processing, and its detection method is:

[0017] Step 1: Image collection: collect the intact and standard yarn hairiness image, and use the image as a reference image;

[0018] Step 2: Establish a detection production line: use the conveyor belt mechanism as the transportation, and then fix the camera mechanism and detection mechanism above the conveyor belt mechanism;

[0019] Step 3: Detection: Place the yarn hairiness on the conveyor belt mechanism, and then transmit the yarn hairiness through the conveyor belt mechanism. When the detection mechanism detects the yarn hairiness, the conveyor belt mechanism stops, and the camera mechanism collects images ;

[0020] Step 4: Image comparison: compare the detected image with the reference image. When there is an error, the yarn hairiness will enter the defective product box, otherwise it will e...

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Abstract

The invention discloses a yarn hairiness defect detection method based on image processing, and relates to the technical field of yarn hairiness. The detection method comprises the following steps: step 1, image acquisition: acquiring a complete and standard yarn hairiness image, and taking the image as a reference image; Step 2, establishing a detection production line: adopting a conveying beltmechanism for conveying, and then fixedly mounting a shooting mechanism and a detection mechanism above the conveying belt mechanism; wherein the yarn hairiness is placed on a conveying belt mechanism, and then the conveying belt mechanism is used for conveying the yarn hairiness; 4, image comparison: comparing the detected image with a reference image; 5, detecting again; according to the invention, image acquisition and comparison can be realized, rapid defect detection is facilitated, and a mixing phenomenon is not liable to occur; and secondary detection can be conveniently realized, the cost can be effectively saved, the stability is high, and the operation is simple and convenient.

Description

technical field [0001] The invention belongs to the technical field of yarn hairiness, and in particular relates to a method for detecting yarn hairiness defects based on image processing. Background technique [0002] Yarn hairiness refers to the fiber ends or loops that protrude from the main body of the yarn. The hairiness index is defined as: on one side of the single-side yarn plane view of the unit yarn length, the cumulative number of hairiness exceeding a certain projection length (vertical distance), the unit is root / 10m (the value of this index is subject to The influence of factors such as test length and projected length). However, the existing yarn hairiness is inspected manually and with specific tools during defect detection, which is prone to defective products mixed in, resulting in low accuracy and complicated operations. Contents of the invention [0003] In order to solve the problem that existing yarn hairiness is checked manually and with specific t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 李东洁陈浩
Owner HARBIN UNIV OF SCI & TECH
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