Printing image defect detection method

A defect detection and image printing technology, which is applied in image analysis, image data processing, measuring devices, etc., can solve the problems of poor template self-adaptation and missed detection, and achieve high-speed detection results

Active Publication Date: 2010-08-11
ZHONG CHAO GREAT WALL FINANCIAL EQUIP HLDGCO +1
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Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies of the prior art, to provide a printing image defect detection method that does not depend on reference templates and is insensitive to changes in real-time imaging brightness, which not only solves the problems of missed detection and false positives in the comparison detection method using reference templates. In addition, it can solve the problem of removing fine wrinkles and spots in the printing and paper industry

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Embodiment Construction

[0019] The preferred embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0020] In a packaging paper printing production line, it is necessary to detect whether there are defects in the printed image. Using the printing image defect detection method of the present invention, such as figure 1 As shown, the specific steps are as follows:

[0021] First, collect the current large image to be detected, perform two-level image search and positioning on the current large image to be detected, select the watermark anti-counterfeiting pattern as the search and positioning template image, and then superimpose the template image on the current large image to be detected, And achieve search and positioning by panning, and search for the template on the large image Figure 1 Consistent specific image blocks, mask out other areas outside the specific image blocks, and create a Mask image.

[0022] The second step is to ca...

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Abstract

The invention discloses a printing image defect detection method, which comprises the following steps: carrying out real-time image model learning in a gray scale region and a gradient region by aiming at specific images on a large image; comparing the large image to be detected to the a gray scale region model and a gradient region model established during learning; realizing small-dimension strong-contrast defect detection through Blob cluster analysis; when no small-dimension strong-contrast defect is detected, dividing the large image into sub regions, and respectively calculating image integrated features; adopting a variable threshold method for carrying out threshold division on each sub region; carrying out Blob cluster analysis on divided images; and realizing the large-area weak-contrast defect detection. Compared with the prior art, the invention does not rely on a reference template, is not sensitive on the real-time imaging brightness change, can overcome the defects of missing detection, error detection and poor self adaptation of the template by using a reference template comparison detection method, and can simultaneously solve the problems of eliminating tiny wrinkles and blackspots in printing and papermaking industries.

Description

technical field [0001] The invention relates to a method for detecting defects in printed images, in particular to a method for detecting defects such as small-sized strong-contrast defects and large-area weak-contrast wrinkles and spots in printed images under high-speed image processing conditions. Background technique [0002] At present, in the application of machine vision in the printing and paper industry, the core method for printing and paper defect detection is mainly image model comparison. Image models are established in different image feature domains through pre-selected specific training sets, and the current image to be detected is compared pixel by pixel with these image models and the contrast difference value is judged. Finally, the detection result is obtained according to the size of the statistical difference value. However, due to objective factors such as light source attenuation, lens contamination, inconsistent flat-field correction coefficients dur...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N21/956G06T7/00
Inventor 张绍兵于勇成苗王竟爽廖世鹏
Owner ZHONG CHAO GREAT WALL FINANCIAL EQUIP HLDGCO
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