Fruit Surface Defect Detection Method Based on Sliding Comparison Window Adaptive Segmentation

Abstract

The invention discloses a fruit surface defect detection method based on the adaptive segmentation of a sliding comparison window. The fruit surface defect detection method comprises the following steps: obtaining an RGB (Red, Green and Blue) colorful image of a fruit, and removing background binaryzation to obtain an initial binaryzation image; extracting and expanding an edge to obtain an outline edge expansion image; removing a background from the RGB colorful image, converting the RGB colorful image into a grayscale image, and establishing a target image; carrying out window scanning and calculation on the grayscale image to obtain a segmentation threshold value; carrying out judgment by the segmentation threshold value, and traversing target image pixels to carry out assignment to obtain a target binaryzation image; and subtracting the outline edge expansion image, and carrying out expansion hole filling corrosion and median filtering processing to obtain the fruit surface defect image. The fruit surface defect detection method is accurate and practical in detection, can effectively avoid dependency on the shapes and the sizes of fruits and agricultural products and avoid complex influences brought by brightness rectification, has a great quantity of application objects and is high in 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 based on sliding comparison window adaptive 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 mod...

AI Technical Summary

Problems solved by technology

[0008] In order to solve the problems in the background technology that the detection of surface defect types is limited and the calculation method is complicated and difficult to be used in online detection or rely on complex hardware imaging technology with high cost, the purpose of the present invention is to provide a fruit that is adaptively segmented based on a sliding comparison window Compared with the background technology, the surface defect detection method has a simpler identification method, more types of surface defect detection and wider object practicability

Images