An image semantic segmentation error annotation data screening method and system

A technology of labeling data and semantic segmentation, applied in the field of image recognition, to achieve the effect of improving data quality, enhancing recognition ability, and optimizing the model

Pending Publication Date: 2019-05-10
贵州宽凳智云科技有限公司
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Problems solved by technology

[0003] The purpose of the present invention is to improve the deficiency in the prior art that the incorrectly labeled data cannot be found from the manually labeled data, and to provide a method and system for screening incorrectly labeled data for image semantic segmentation

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  • An image semantic segmentation error annotation data screening method and system
  • An image semantic segmentation error annotation data screening method and system
  • An image semantic segmentation error annotation data screening method and system

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Embodiment Construction

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

[0027] see figure 1 , the present embodiment provid...

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Abstract

The invention relates to an image semantic segmentation error annotation data screening method and system. The method comprises the following steps: carrying out image recognition prediction on an original image participating in training by utilizing a trained network model to obtain a confidence coefficient of each pixel point in the original image, and calculating according to the confidence coefficient to obtain an accuracy rate, a recall rate and / or a cross-to-parallel ratio of a target category; judging whether the annotation data are suspicious error annotations or not by utilizing the obtained accuracy rate, recall rate and / or cross-merging ratio; and screening out suspected mistakenly labeled data. According to the method and the system, data with wrong annotations can be screenedout and can be used for secondary annotation and training, and an original model is optimized, so that the model recognition capability is improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method and system for screening incorrectly labeled data of image semantic segmentation. Background technique [0002] In recent years, with the gradual development of deep learning, convolutional neural networks have been widely used in the field of computer vision, including target detection, image semantic segmentation and so on. The training process of the convolutional neural network model is: collecting sample data - labeling the sample data - inputting the sample data for model prediction - calculating the loss of the prediction result and the labeled data, and optimizing the model parameters based on the calculation result - N iterations to get the final model. The training of the model requires a large amount of sample data, and the quality of the training data directly affects the quality of the model. Therefore, in the process of using the neural network ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 高三元张本兴陈慧贞
Owner 贵州宽凳智云科技有限公司
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