Data annotation correction method based on frame regression

A correction method and frame technology, which is applied in the field of deep learning, can solve the problems of inconsistency in the labelling of labelers' subjective consciousness, large workload of data labeling and correction, and difficulty in labeling difficult samples, so as to improve the diversity of data distribution and save labeling time. , the effect of improving the quality of data annotation

Active Publication Date: 2021-06-01
聚时科技(江苏)有限公司
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Problems solved by technology

[0005] According to the above analysis, the following problems still exist in the field of deep learning data labeling: 1. The workload of data labeling and correction is huge and the efficiency is low; 2. The difficulty of labe

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  • Data annotation correction method based on frame regression
  • Data annotation correction method based on frame regression
  • Data annotation correction method based on frame regression

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[0034] In order to make the object, technical solution and beneficial technical effects of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific implementations described in this specification are only for explaining the present invention, not for limiting the present invention.

[0035] It is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer" etc. indicate an orientation or The positional relationship is based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, Therefore, it should n...

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Abstract

A data annotation correction method based on frame regression comprises the following steps: dividing data into two batches of sample data, which are gold annotations and hard annotations, according to the difficulty degrees in the first annotation process and the confidence coefficients of an annotation result; using a focus loss function to improve a target detection algorithm YOLO V5, then using gold annotation sample data for training, and after training of the training models is stable, saving m training models every fixed iteration times; reasoning the stored m training models on the hard annotation sample data, and performing offline storage on all pictures formed according to the reasoning result; for each picture, summarizing all reasoning results of the m training models, clustering all frames, and the number of clusters being set to be the number of real targets on the current picture; counting the number of frames, and carrying out general distribution modeling on four boundary points of all frames in the same cluster; and correcting the position of the frames according to a modeling result.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a method for correcting data labeling based on frame regression. Background technique [0002] At present, artificial intelligence technology with deep learning as the core has made breakthroughs in the fields of industrial vision, natural language processing, and automatic driving. Among them, in the field of industrial quality inspection, the precision of convolutional neural network for defect classification has surpassed that of human eyes, and the recognition speed of defects is far faster than that of human beings. The detection scheme and equipment have entered the stage of industrialization. [0003] Deep learning is an algorithmic weapon in the era of big data. It has algorithmic performance that is difficult to surpass by traditional machine learning. However, deep learning relies heavily on training data. In actual industrial scenarios, it is difficult to obtai...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/04G06N3/08
Inventor 糜泽阳郑军
Owner 聚时科技(江苏)有限公司
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