Color sorting method

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a color sorting method. The color sorting method comprises the following steps: obtaining an original image of a material through a camera or a video camera, wherein the color of the material and a background color in the original image have a chromatic aberration convenient for identification; independently taking a value range of the background color and the value range of a spoiled material color as threshold values, and carrying out binarization processing on the original image to obtain a first black-and-white image and a second black-and-white image; and carrying out background expansion processing on the first black-and-white image, wherein the value of the structural element of the expansion processing is greater than or equal to the size of a material shadow; and carrying out intersection processing on the second black-and-white image and the image subjected to the expansion processing, and labeling and picking out the image subjected to the intersection processing to cause qualified products to remain. Through a series of processing, the edge shadow of the large-particle material is excluded, only whether the spoiled material color is in the presence in the material or not is judged, erroneous judgment caused by that the shadow is identified as the spoiled material color by mistake can be avoided, and the color sorting precision of the large-particle material is greatly improved after the large-particle material is processed in the above way.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a color sorting method. Background technique [0002] Color sorter is an automatic sorting equipment that sorts materials mainly according to color. Originally, the color sorter was mainly used for color sorting of rice, but now it has been widely used for color sorting of miscellaneous grains, dehydrated vegetables, waste plastics, ores and other materials. [0003] When color sorting some materials with small particles such as rice and mung beans, it is easier to distinguish between good materials and bad materials, because the material particles are small, as long as the color of a certain material particle is all or part of the color of the bad material, that is It can be judged that the particles are bad materials. However, when color sorting larger particles such as garlic, peanuts, large white kidney beans, ores, and waste plastic sheets, it becomes more complic...

AI Technical Summary

Problems solved by technology

[0004] One kind of interference comes from shadows and reflections during the imaging process. Due to the thickness of large particle materials and the uneven surface, the light source of the color sorter cannot achieve uniform illumination in all directions, so the edge of the image formed by large particle materials There will be shadows or reflections, which can be easily identified as bad material colors when judging

For example, for materials such as garlic and peanuts, if there are black spots, it is a bad material, and the color of the shadow of the material is also gray-black, which is basically the same color as the black spots, so the shadow of the good material on the image will cause the color sorter to Misjudgment, which eventually led to the good material being wrongly selected

The other is the interference from a small amount of variegated colors on large particle materials, for example, there will be writings of other colors on the fragments of plastic bottle caps or a small amount of variegated spots on some materials, these writings or spots will not affect the quality of the materials , but during color sorting, a small amount of variegated writing or spots on these good materials will also cause misjudgment by the color sorter

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