Cotton image segmentation method and system based on morphological reconstruction and adaptive threshold

A technology based on morphology and adaptive threshold, which is applied in the field of image processing, can solve the problems of long model training time, large amount of calculation, and difficulty in accurately segmenting cotton, so as to eliminate the influence of lighting factors, uniform grayscale characteristics, and increase The effect of contrast

Pending Publication Date: 2021-05-28
SHANDONG UNIV +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] At present, the segmentation method of cotton target is still in the stage of research and exploration: many traditional image processing methods are greatly affected by illumination and environmental background, and it is difficult to accurately segment cotton in various complex natural environments; image segmentation methods based on deep learning require a large number of It is based on the marked image, and the amount of calculation is large, the model training time is long, the hardware configuration is high, and it is difficult to apply to the segmentation of cotton images

Method used

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  • Cotton image segmentation method and system based on morphological reconstruction and adaptive threshold
  • Cotton image segmentation method and system based on morphological reconstruction and adaptive threshold
  • Cotton image segmentation method and system based on morphological reconstruction and adaptive threshold

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Embodiment 1

[0033] This embodiment provides a cotton image segmentation method based on morphological reconstruction and adaptive threshold;

[0034] Such as figure 1 As shown, the cotton image segmentation method based on morphological reconstruction and adaptive threshold includes:

[0035] S101: Acquire the cotton image to be processed; convert the cotton image to be processed from the RGB color space to the HSV color space;

[0036] S102: Extract the saturation S component of the cotton image in the HSV color space;

[0037] S103: Perform filtering processing on the saturation component image to remove random noise in the image;

[0038] S104: perform morphological reconstruction on the filtered image to remove dark spots and blemishes in the image;

[0039] S105: Perform grayscale transformation on the image after morphological reconstruction to enhance the contrast between the cotton area and the background area;

[0040] S106: Perform threshold segmentation on the image after t...

Embodiment 2

[0098] The present embodiment provides a cotton image segmentation system based on morphological reconstruction and adaptive threshold;

[0099] Cotton image segmentation system based on morphological reconstruction and adaptive threshold, including:

[0100] The acquisition module is configured to: acquire the cotton image to be processed; convert the cotton image to be processed from the RGB color space to the HSV color space;

[0101] A saturation component extraction module configured to: extract the saturation S component of the cotton image in the HSV color space;

[0102] A filtering module configured to: filter the saturation component image to remove random noise in the image;

[0103] A morphological reconstruction module configured to: perform morphological reconstruction on the filtered image to remove dark spots and blemishes in the image;

[0104] A grayscale transformation module configured to: perform grayscale transformation on the image after morphological ...

Embodiment 3

[0110]This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0111] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or...

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Abstract

The invention discloses a cotton image segmentation method and system based on morphological reconstruction and an adaptive threshold. The method comprises the steps of obtaining a to-be-processed cotton image; converting the to-be-processed cotton image from an RGB color space to an HSV color space; extracting a saturation S component of the cotton image of the HSV color space; performing filtering processing on the saturation component image to remove random noise in the image; performing morphological reconstruction on the filtered image to remove dark spots and flaws in the image; carrying out gray level transformation on the image after morphological reconstruction processing so as to enhance the contrast ratio between the cotton area and the background area; and carrying out threshold segmentation on the image after gray level transformation to obtain a segmentation result of the cotton image. And the segmentation precision of the cotton in the natural environment is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a cotton image segmentation method and system based on morphological reconstruction and adaptive threshold. Background technique [0002] The statements in this section merely mention the background technology related to the present invention and do not necessarily constitute the prior art. [0003] With the rapid development of science and technology, intelligent picking robots are increasingly used in agricultural production. The research and development of cotton picking robots has great practical significance and broad application prospects. The first problem to be solved by the cotton picking robot in the process of picking cotton is to segment the cotton image from the complex environmental background. The quality of cotton image segmentation will directly affect the accuracy of the picking system. [0004] At present, the segmentation method of cotton target is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/136G06T7/155G06T5/00
CPCG06T7/11G06T7/136G06T5/005G06T7/155G06T5/002
Inventor 杨公平王冲孙启玉宋成秀褚德峰张同心
Owner SHANDONG UNIV
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