Intelligent image annotation method

A technology of intelligent images and image files, applied in image enhancement, image analysis, image data processing, etc., can solve the problems of low data labeling efficiency, achieve the effects of shortening labeling time, improving labeling work, and improving efficiency

Pending Publication Date: 2021-06-04
中科海拓(无锡)科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The main purpose of the present invention is to provide an intelligent image labeling method, which can effectively solve the problem of low data labeling efficiency in the background technology

Method used

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  • Intelligent image annotation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0032] Such as figure 1 As shown, an intelligent image labeling method includes the following steps:

[0033] Step 1: Obtain a database of image files to be marked, and select an image file as an OK sample;

[0034] Step 2: judge whether the match with the OK sample is successful;

[0035] Step 3: Manually compare the similarity between the matched file and the OK sample file;

[0036] Step 4: If the threshold value is appropriate and no adjustment is required, continue the previous operation; if the threshold value is not appropriate, it needs to be adjusted before repeating the previous operation, and then operate according to the similarity.

[0037] Step 1 includes the following sub-steps:

[0038] The first step: scramble the unlabeled data set, and then perform random extraction to extract a certain number of image data files;

[0039] The second step: mark the relevant defects on the extracted image data files;

[0040] The third step: classify the marked image dat...

Embodiment 2

[0051] Such as figure 1 As shown, an intelligent image labeling method includes the following steps:

[0052] Step 1: Obtain a database of image files to be marked, and select an image file as an OK sample;

[0053] Step 2: judge whether the match with the OK sample is successful;

[0054] Step 3: Manually compare the similarity between the matched file and the OK sample file;

[0055] Step 4: If the threshold value is appropriate and no adjustment is required, continue the previous operation; if the threshold value is not appropriate, it needs to be adjusted before repeating the previous operation, and then operate according to the similarity.

[0056] Step 1 includes the following sub-steps:

[0057] Step 1: Scramble the unlabeled data set, and then perform random extraction to extract a certain number of image data files; for example, if a certain amount of image data is extracted, then the remaining data sets can be set as samples to be extracted pond;

[0058] The se...

Embodiment 3

[0080] Such as figure 1 As shown, an intelligent image labeling method includes the following steps:

[0081] Step 1: Obtain a database of image files to be marked, and select an image file as an OK sample;

[0082] Step 2: judge whether the match with the OK sample is successful;

[0083] Step 3: Manually compare the similarity between the matched file and the OK sample file;

[0084] Step 4: If the threshold value is appropriate and no adjustment is required, continue the previous operation; if the threshold value is not appropriate, it needs to be adjusted before repeating the previous operation, and then operate according to the similarity.

[0085] In step 2, judge whether the match with the OK sample is successful. The specific operation is that you only need to run the script tool_retrieve.py, and follow the samples in the sample folder in one step, and automatically cut to the target folder.

[0086] In step 2, match the image data to be labeled with the sample data...

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PUM

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Abstract

The invention discloses an intelligent image annotation method, which comprises the following steps of: 1, obtaining a to-be-annotated image file database, and selecting an image file as an OK sample; 2, judging whether the image file is successfully matched with an OK sample or not; 3, manually comparing the similarity between the matched file and the OK sample file; 4, if the threshold value is appropriate and does not need to be adjusted, continuing to execute the previous operation; if the threshold value is not appropriate, repeating the previous operation after adjustment, and then performing the operation according to the similarity. The intelligent image labeling method is suitable for the characteristic of target classification of the industrial image data, overcomes the problems of large data volume, long labeling time, insufficient training time and the like of the industrial image data in manual labeling, and can better perform labeling work of the industrial image data.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to an intelligent image labeling method. Background technique [0002] Image annotation plays a pivotal role in industrial vision. The main goal of image annotation is to complete the labeling of specific labels required by the task. This can include labeling of image segmentation, labeling of image recognition, and labeling of image defects; among them, the work of labeling image segmentation and image recognition is to use labeling software to draw labels on images (segmentation is generally marked with borders, recognition Usually irregular polygons), sometimes even labeling pixel-level labels. [0003] The general task requirements of computer vision are the following: object detection, line / edge detection, segmentation, key point recognition, image classification. Object detection generally uses two types of 2D detection and 3D detection. Among them, 2D detection is u...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
CPCG06T7/0004G06T2207/20081G06F18/22G06F18/214
Inventor 谢传鹏程坦刘涛吕剑
Owner 中科海拓(无锡)科技有限公司
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