Image segmentation method, apparatus, and computer-readable storage medium

An image segmentation and image technology, applied in image analysis, computer components, calculations, etc., can solve the problems of inaccurate coverage of pixels, inability to obtain accurate posture, inaccurate positioning, etc., to achieve accurate posture, high efficiency and accuracy Accurate effect of classification and positioning

Active Publication Date: 2019-01-04
SHENZHEN DORABOT ROBOTICS CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the existing image segmentation algorithms usually only calculate a roughly rectangular border, which cannot accurately cover all

Method used

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  • Image segmentation method, apparatus, and computer-readable storage medium
  • Image segmentation method, apparatus, and computer-readable storage medium
  • Image segmentation method, apparatus, and computer-readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0041] Example one

[0042] This embodiment provides an image segmentation method for object segmentation in a logistics system.

[0043] Please see figure 1 , The image segmentation method includes:

[0044] Step S101: Obtain the original image.

[0045] Step S102: Input the original image to a pre-trained neural network for calculation, calculate and segment each object in the first image, and obtain a pixel point set corresponding to each object.

[0046] In this embodiment, the original image is obtained first. In this embodiment, the original image is a two-dimensional RGB image.

[0047] After the original image is obtained, the original image is input to a pre-trained neural network for calculation, and each object in the first image is calculated and classified to obtain a pixel point set corresponding to each object. Among them, the original image is used as the input of the neural network. The pre-trained neural network can calculate the output value according to the calcula...

Example Embodiment

[0050] Example two

[0051] This embodiment provides an image segmentation method. This embodiment further explains step S102 on the basis of the above embodiment, please refer to figure 2 ,details as follows:

[0052] please check figure 2 In step S102, inputting the original image to a pre-trained neural network for calculation, calculating and classifying each object in the first image, and obtaining a pixel set corresponding to each object includes:

[0053] Step S201: Obtain a dimensionality reduction image of the original image through a neural network dimensionality reduction algorithm on the original image;

[0054] Step S202: Classify each pixel of the reduced-dimensional image according to the reduced-dimensional image and a preset classification model;

[0055] Step S203: Pass the dimensionality reduction image through a neural network dimensionality increase algorithm to obtain a classified image with the same size as the original image.

[0056] In this embodiment, the or...

Example Embodiment

[0064] Example three

[0065] This embodiment proposes an image segmentation method. This embodiment is based on the above-mentioned embodiment with additional steps. details as follows:

[0066] The image segmentation method further includes:

[0067] Step S301: Obtain multiple training images;

[0068] Step S302: Obtain the label of the object whose integrity reaches 70% in the training image according to the input instruction;

[0069] Step S303, training the neural network according to the training image and the corresponding annotation.

[0070] In this embodiment, multiple training images are obtained. Among them, there can be thousands of training images, and the more training images can train the more accurate classification model. Specifically, the multiple training models include objects in multiple shapes, multiple angles, multiple distances, and or multiple light rays. Thus, the training result can be more robust.

[0071] Then, it is manually labeled, that is, the pixels ...

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Abstract

The invention discloses an image segmentation method, an apparatus and a computer-readable storage medium. The original image is inputted to a pre-trained neural network for calculation, and each object in the first image is classified to obtain a pixel point set corresponding to each object. The invention has the effects of accurately dividing each pixel of an object to achieve accurate positioning and accurate attitude judgment.

Description

technical field [0001] The invention relates to the field of robot sorting, in particular to an image segmentation method, device and computer-readable storage medium. Background technique [0002] At present, with the development of logistics automation, robot sorting is becoming more and more popular. More and more goods need to be sorted quickly. In actual operation, many goods are stacked together and need to be distinguished. [0003] However, the existing image segmentation algorithm usually only calculates a roughly rectangular frame, which cannot accurately cover all the pixels of the object, contains a large amount of background information, and causes inaccurate positioning and cannot obtain an accurate pose. Contents of the invention [0004] The main purpose of the present invention is to provide an image segmentation method, device and computer-readable storage medium, aiming at accurately segmenting each pixel of an object so as to achieve accurate position...

Claims

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Application Information

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IPC IPC(8): G06T7/10G06K9/62
CPCG06T7/10G06T2207/20081G06T2207/20084G06F18/213G06F18/24
Inventor 吕仕杰
Owner SHENZHEN DORABOT ROBOTICS CO LTD
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