AI (Artificial Intelligence) based low-confidence sample processing method and system of board sorting

A low-confidence, processing method technology, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve the problems of low-confidence samples, limited source of wooden boards, judgment and classification, etc., to improve training efficiency and improve sorting. The effect of accuracy and high classification accuracy

Active Publication Date: 2018-03-23
BEIJING WOOD AI TECH LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the above board sorting scenario, the classification of each factory is customized, and its board source is also limited
Therefore, the acquisition of training data is difficult to be easily satisfied
When the machine learning method is running, a sample with low confiden

Method used

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  • AI (Artificial Intelligence) based low-confidence sample processing method and system of board sorting
  • AI (Artificial Intelligence) based low-confidence sample processing method and system of board sorting
  • AI (Artificial Intelligence) based low-confidence sample processing method and system of board sorting

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0074] Example 1

[0075] Such as figure 1 As shown, a wooden board is sent into the image collection area through a conveyor belt. The wooden board completes image acquisition during its movement. The imaging device collects an image of the wooden board and inputs the collected image into a trained machine learning model.

[0076] For the machine learning method, first, a part of the wood board samples need to be obtained inside the factory, and the classification of each wood board sample. Because wood is a semi-natural product, it is impossible to have a clear classification standard like steel and other industrial products. Therefore, currently in the factory, custom classification is carried out according to the actual situation of the factory. This custom classification method is more suitable for the actual situation and classification requirements of different wood board factories, and the classification is more flexible and convenient. The specific implementation of clas...

Example Embodiment

[0097] Example 2

[0098] In the process of manual labeling, because the low-confidence samples themselves have certain ambiguities, even manual classification faces certain challenges. Therefore, how to better present these samples to the operator determines the labeling accuracy of low-confidence samples. Here, we introduce the following implementation methods to describe specific presentation methods.

[0099] Such as image 3 As shown, not only the image data of the low-confidence samples are presented, but also the image data of the high-confidence samples are presented at the same time. By comparing the high-confidence sample data and the low-confidence sample data at the same time, it makes the comparison easier for the operator , Re-calibrate the low-confidence sample image.

[0100] In order to make the comparison clearer, low-confidence samples and high-confidence samples can be displayed in the same interface, or manually labeled samples. In the interface of this method...

Example Embodiment

[0102] Example 3

[0103] There is a possibility that the generation of low-confidence samples may be caused by changes in external ambient light, such as insufficient light intensity, or other light pollution entering the collected image. Therefore, one method preprocesses the sample, for example, by enhancing the original image based on the reference image, for example, normalizing parameters such as brightness, white balance, and contrast.

[0104] In order to eliminate the influence of illumination changes on image quality later, you can set a reference image during the image acquisition process. For example, in the image collection area, a white reference object is provided to ensure that the image of the wood sample and the image of the white reference object are collected at the same time. The white reference object can be used to provide a reference for white balance, brightness, or other image parameters. In one method, an external light source, such as an LED light sour...

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Abstract

The invention provides an AI based low-confidence sample processing method and system of board sorting. Image data of at least one format of a low-confidence sample is obtained; an image of at least one format of the low-confidence sample is presented in a display device; a new class marked by the low-confidence sample is obtained; and a training method is input to the marked low-confidence sample, and a new classification model is obtained via re-training. According to the method and system, the low-confidence sample can be discovered continuously and utilized, so that the classification precision of a machine learning method is improved gradually.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a processing method and system for low-confidence samples in machine learning, a method for classifying and labeling image samples in machine learning, a system and a computer program product thereof. Background technique [0002] In the field of wood processing, wood sorting is an important link. After the logs are shaped, colored, dried and other processes, they become processed wood boards. Before wooden boards become commercialized products, they need to be classified according to different board characteristics. In traditional methods, the sorting of the boards is done manually. Trained workers, through observation, judge the color, texture, and defects of each plank, and then classify a plank into different categories based on experience. The wood boards in each category have closer characteristics, achieving higher consistency in product appe...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2415G06F18/214
Inventor 丁磊
Owner BEIJING WOOD AI TECH LTD
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