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Fast local threshold segmentation method for fruit surface defect detection

A local threshold and defect detection technology, applied in the directions of optical testing flaws/defects, measuring devices, analyzing materials, etc., can solve the problems of difficult online detection, high cost, complicated calculation methods, etc., and achieve accurate and practical detection, fast speed, Overcome the effects of distractions

Inactive Publication Date: 2017-08-11
ZHEJIANG UNIV
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Problems solved by technology

[0008] In order to solve the problems in the background technology that the types of detection surface defects are limited and the calculation methods are complicated and difficult to be used for online detection or rely on complex hardware imaging technology with high cost, the purpose of the present invention is to provide a fast local threshold segmentation for detection of fruit surface defects method, suitable for online detection occasions

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  • Fast local threshold segmentation method for fruit surface defect detection
  • Fast local threshold segmentation method for fruit surface defect detection
  • Fast local threshold segmentation method for fruit surface defect detection

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Embodiment Construction

[0055] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0056] Such as figure 1 Shown, embodiment of the present invention and its implementation process are as follows:

[0057] 1) Take a sample fruit RGB color image, such as figure 2 shown.

[0058] 2) Remove the background from the RGB color image of the fruit and convert it into a grayscale image, as shown in image 3 The target image P(i,j) shown.

[0059] 3) Use formula (1) to obtain the target image P(i, j) as Figure 4 The integral image I(x,y) shown is:

[0060]

[0061] 4) Scan each pixel of the target image P(i, j), and scan the corresponding pixels in the integral image with a 25×25 window at the same time. The pixels in the window beyond the boundary of the integral image are filled with boundary pixels, and calculate each 25×25 window The arithmetic mean value of is used as the segmentation value Q(i,j) of each pixel of the t...

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Abstract

The invention discloses a fast local threshold segmentation method for fruit surface defect detection. An RGB color image of a fruit is obtained, the RGB color image of the fruit is subjected to background removal and converted into a grayscale image, a target image is formed, the target image is converted into an integral image, the integral image is used for calculation, the target image is processed to obtain a target binary image, hole filling and median filtering processes are conducted in sequence to obtain a fruit surface defect image, and according to the fruit surface defect image, a defect result is obtained. The detection method is accurate and practical and can effectively avoid brightness rectifying of the nearly spherical fruit image, the image calculation speed is high, the application range is wide, and the method had great application value.

Description

technical field [0001] The invention relates to a computer vision image processing method, in particular to a fruit surface defect detection method for rapid local threshold segmentation. Background technique [0002] Surface defect detection is one of the important basis for fruit grading, which is strictly regulated in the fruit grading standards of countries all over the world. A large number of scholars at home and abroad have studied the detection of surface defects of fruits and agricultural products by means of computer vision. However, many agricultural products are spherical, and the gray value in the middle of the two-dimensional graphics is much larger than the gray value of the edge, which leads to difficulties in the detection of surface defect images. [0003] After searching the existing technologies, it is found that the methods are mainly divided into three categories: [0004] 1) The processing method based on the spherical gray scale model. For example, ...

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Application Information

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IPC IPC(8): G01N21/88
CPCG01N21/8851G01N2021/8887
Inventor 应义斌容典饶秀勤
Owner ZHEJIANG UNIV
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