Surface defect detection method based on visual saliency map and support vector machine

A support vector machine and defect detection technology, applied in the field of surface defect detection, can solve the problems of high labor intensity, the influence of subjective factors of inspectors, and low work efficiency, and achieve the effect of high classification accuracy

Active Publication Date: 2014-12-10
苏州佳赛特智能科技有限公司
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Problems solved by technology

At present, most domestic manufacturers use manual visual inspection to complete the inspection work. This meth

Method used

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  • Surface defect detection method based on visual saliency map and support vector machine
  • Surface defect detection method based on visual saliency map and support vector machine
  • Surface defect detection method based on visual saliency map and support vector machine

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Embodiment 1

[0024] Embodiment one: see figure 1 As shown, a surface defect detection method based on visual saliency map and support vector machine includes the following steps:

[0025] (1) Extracting features from the visual saliency map:

[0026] (1) For 250 surface image samples of the product to be tested, the GBVS model is used to calculate their respective visual saliency maps; figure 2 As shown in (a), it represents the original cloth image. By extracting the brightness feature and direction feature, the visual saliency map of each image is obtained by using the GBVS model ,Such as figure 2 (b) shown.

[0027] (2) Through the maximum between-class variance method (OTSU), the visual saliency map obtained in step (1) is subjected to adaptive threshold segmentation to obtain a binary image , to extract its visual background area, such as figure 2 as shown in (c);

[0028] Let the grayscale in the image be The number of pixels is , the grayscale range is Pixels in the...

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Abstract

The invention discloses a surface defect detection method based on a visual saliency map and a support vector machine. The method comprises the following steps: firstly, calculating the visual saliency map of a surface image sample of a product to be detected by using a GBVS (Graph-based Visual Saliency) model, carrying out adaptive threshold segmentation on the visual saliency map through a method of maximum classes square error, extracting a visual saliency region, calculating the average gray-values of the visual saliency map and the average gray-values of the visual saliency region in the visual saliency map, and respectively carrying out normalization processing to form two-dimensional features, then taking the obtained two-dimensional features of the visual saliency map as training samples of the support vector machine, selecting two dimensions to classify the optimal classification line, based on the optimal classification line, classifying the two-dimensional features, thereby distinguishing whether the product in the map has defects or not. The surface defect detection method disclosed by the invention can effectively save labor, lower the labor intensity and improve the work efficiency and has high identification accuracy.

Description

technical field [0001] The invention relates to a surface defect detection method, in particular to a surface defect detection method based on a visual saliency map and a support vector machine. Background technique [0002] In textile, metal and other production fields, how to detect and solve product surface defects in time is a widely concerned issue. At present, most domestic manufacturers use manual visual inspection to complete the inspection work. This method is labor-intensive, has low work efficiency, and is easily affected by the subjective factors of inspectors. Therefore, the research on automatic surface defect detection technology is of great significance. [0003] Visual saliency model is one of the research hotspots of domestic and foreign scholars in recent years, and it is widely used in remote sensing, metallurgy, textile, agricultural production and other fields. This model imitates the human visual attention mechanism and integrates image features thro...

Claims

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Application Information

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IPC IPC(8): G01N21/88
Inventor 何志勇胡佳娟杨宏兵翁桂荣孙立宁左保齐王晨
Owner 苏州佳赛特智能科技有限公司
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