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Semi-automatic labeling method and system based on human-computer interaction

A human-computer interaction and semi-automatic technology, applied in the field of target detection, can solve the problems of easy error detection or missed detection, and achieve the effect of getting rid of time-consuming and laborious, improving detection effect and good migration effect

Active Publication Date: 2021-06-15
TSINGHUA UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

This method can give the picture a position information to detect the target when the Faster rcnn misses the target, and overcomes the influence of the Faster rcnn when detecting the target due to lighting conditions, occlusion, shadows, etc. question

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  • Semi-automatic labeling method and system based on human-computer interaction
  • Semi-automatic labeling method and system based on human-computer interaction
  • Semi-automatic labeling method and system based on human-computer interaction

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Embodiment Construction

[0044] In order to make the purpose, technical solution and advantages of the application clearer, the technical solution of the application will be clearly and completely described below in conjunction with specific embodiments of the application and corresponding drawings. It should be understood that the described embodiments are only some of the embodiments of the present application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0045] Before introducing the embodiments of the present invention, the relevant terms involved in the embodiments of the present invention are first explained as follows:

[0046] RGB image: refers to the color image collected by a monocular camera, which is a three-channel image.

[0047] Label: Indicates the category label used for the supervised training of ...

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Abstract

The invention discloses a semi-automatic labeling method and system based on human-computer interaction. The method comprises the following steps: fusing an RGB image to be labeled with a generated first Gaussian heat map; preprocessing the fused image; inputting the preprocessed fusion image into a pre-established and trained semi-automatic labeling model, and labeling a plurality of prediction frames on the RGB image to be labeled; and when a prediction frame does not meet the requirement, correcting the prediction frame which does not meet the requirement by generating a second Gaussian heat map. According to the method, the Gaussian heat map is used as prior information to detect the target, so that the expected effect of semi-automatic labeling is achieved; the defect that time and labor are wasted by manual labeling can be overcome, and the labeling precision is improved.

Description

technical field [0001] The invention belongs to the field of target detection, and in particular relates to a method for using additional clicks as prior information to achieve labeling purposes through fusion with RGB images, and in particular to a semi-automatic labeling method and system based on human-computer interaction. Background technique [0002] With the rapid development of technologies such as the Internet, machine learning, big data, and cloud computing, all kinds of information data continue to grow at an exponential rate. industry applications. Labeling data sets is an important step for deep learning, but labeling data is a tedious task, and semi-automatic labeling can reduce the workload. Even though there are open source semi-automatic labeling tools, the premise of using them is that the higher the accuracy of the model, the better. If the detection results are inaccurate, it will increase the workload and make it unusable. Moreover, the use of semi-aut...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/40G06F18/241
Inventor 张新钰李骏李志伟刘宇红王力卢一倩
Owner TSINGHUA UNIV
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