Tire defect detection method based on singular value decomposition

A singular value decomposition and defect detection technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of missed detection and false detection of tire defects, hidden dangers of vehicle safety, etc., to avoid missed detection and false detection, improve The effect of accuracy

Active Publication Date: 2017-09-22
SHANDONG UNIV OF FINANCE & ECONOMICS
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Problems solved by technology

However, in the actual inspection process, the visual fatigue of workers will cause missed

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  • Tire defect detection method based on singular value decomposition
  • Tire defect detection method based on singular value decomposition
  • Tire defect detection method based on singular value decomposition

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[0040] In order to make the purpose, features and advantages of the present invention more obvious and understandable, the technical solutions protected by the present invention will be clearly and completely described below using specific embodiments and accompanying drawings. Obviously, the implementation described below Examples are only some embodiments of the present invention, but not all embodiments. Based on the embodiments in this patent, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this patent.

[0041] The present invention provides a tire defect detection method based on singular value decomposition, such as figure 1 As shown, tire defect detection methods include:

[0042] Step 1: For the image to be inspected that contains n pixels, divide each pixel and the pixels in the surrounding m×m neighborhood into an image block, and obtain n image blocks in total, denoted as P i ,(i=...

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Abstract

The invention provides a tire defect detection method based on singular value decomposition. A to-be-detected image I containing n pixels is divided to n m*m image blocks; each image block Pi is converted to a column vector ci; column vectors corresponding to all image blocks are used to construct an image block matrix M; through sequentially calculating the ratio of adjacent two singular values, the rank r of the image block matrix M is determined; the former r maximum singular values of the image block matrix M and the corresponding left singular vectors and right singular vectors are used to reconstruct a low-rank approximation matrix Mr of the image block matrix M; each column vector in the low-rank approximation matrix Mr is converted to an image block; all image blocks P<r> are used to reconstruct an approximate image Ir of the to-be-detected image I; hard threshold segmentation is carried out on a residual image I-Ir, a binary image Ib is obtained, and coordinates corresponding to a pixel with a gray value to be one in the image are the defect position. The detection method can position the specific defect position, missed detection and error detection caused by manual operation are avoided, and the defect detection accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a tire defect detection method based on singular value decomposition. Background technique [0002] Tires are an important part of a car, and its quality directly affects the safety of the vehicle when driving. Usually, a complete tire consists of four regions: crown region, shoulder region, sidewall region and reinforcement region. Affected by factors such as raw materials, processing equipment, and production techniques, a small number of tires may have different types of defects in certain component areas. The defects of tires are mainly concentrated in three parts: sidewall, tire crown and tire shoulder. Common defects include impurities, lap joints and air bubbles. These two types of defects, impurities and laps, will destroy the cord structure inside the tire, resulting in uneven force on the tire and causing a tire blowout; while air bubbles destroy the adhesion...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06T7/136
CPCG06T7/0008G06T7/10G06T7/136G06T2207/30108
Inventor 郭强刘慧张彩明
Owner SHANDONG UNIV OF FINANCE & ECONOMICS
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