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Fabric defect detection method based on optical threshold segmentation

An optimal threshold and detection method technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as inability to segment out defects

Inactive Publication Date: 2014-05-07
ZHONGYUAN ENGINEERING COLLEGE
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Since this type of method only considers the variance between classes, when the texture of the fabric image is complex or the illumination is uneven, the defects cannot be correctly segmented

Method used

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  • Fabric defect detection method based on optical threshold segmentation
  • Fabric defect detection method based on optical threshold segmentation
  • Fabric defect detection method based on optical threshold segmentation

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Embodiment

[0085] The sample image used in the embodiment is a grayscale image (if it is a color image, convert it to a grayscale image). figure 2 The fabric samples shown in a and 3a all have stain defects. Fabric sample 2a is affected by lighting conditions, and the image is bright and uneven; fabric sample 3a has relatively complex textures on the fabric surface in addition to uneven illumination. The detection results using the Otsu algorithm, the valley emphasis method and the algorithm in this paper are as follows: figure 2 b, 2c, 2d and image 3 b, 3c, 3d shown. It can be seen from the figure that for fabric samples 2a and 3a, the maximum inter-class variance method and the valley emphasis method cannot detect defects correctly due to the influence of light and the complexity of fabric surface texture, while the proposed algorithm of the present invention can correctly detect defects. Flaws.

[0086] Figure 4 The fabric sample shown in a has large-diameter defects, the sur...

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Abstract

The invention discloses a fabric defect detection method based on optical threshold segmentation. The method comprises two parts, i.e., an optimal threshold selection part and an image segmentation part. The optimal threshold selection part specifically comprises the following steps: step 1, randomly selecting a gray value as a segmentation threshold, step 2, according to the threshold, dividing an image into two types, step 3, solving the gray scale total mean value of the gray scale mean value of each type of image and the image, step 4, solving a between-class variance and an inter-class variance of the two types, step 5, calculating a weight, and step 6, calculating an optimal segmentation threshold according to a discrimination formula. The image segmentation part is carried out on the basis of the optimal threshold selection part. The gray value of each pixel point in the image is compared to the optimal threshold, 225 is taken as a gray value which is greater than the threshold, and 0 is taken as a gray value which is smaller than the threshold, such that the image can be divided into a background part and an object part. According to the invention, during segmentation threshold selection, the between-class variance of pixels is considered, an inter-class distance is also introduced, and the weight is employed for reinforcement, so that the calculated threshold is more accurate, the defects of a fabric image can be accurately detected, and the segmentation effect of a complex texture image is improved.

Description

technical field [0001] The invention relates to a method for detecting a defect in a fabric image, in particular to detecting and locating a defect in a fabric defect image using an optimal threshold segmentation method, and belongs to the field of textile image processing. Background technique [0002] In textile products, fabric defects are an important factor affecting textile prices. According to market research, the price of defective products should be reduced to 45%-53% of the normal product price. Therefore, fabric defect detection is a key link in the fabric production process. The traditional detection of fabric defects is done manually, the speed of manual detection is slow, the rate of missed detection and false detection is high, and the detection results are greatly affected by the subjectivity of workers, which cannot meet the requirements of real-time detection. [0003] In recent years, with the development of computer and image processing technology, the ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 刘洲峰李春雷董燕廖亮王九各闫磊
Owner ZHONGYUAN ENGINEERING COLLEGE
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