Training method and device for image segmentation model under label fault tolerance and related equipment

Abstract

The invention relates to a training method and a training device for an image segmentation model under label fault tolerance and related equipment. The method comprises the steps of obtaining a training sample set; processing the sample image through a segmentation model to obtain a prediction segmentation result; determining a segmentation loss function according to the prediction segmentation result and the pixel-level annotation of the sample image; processing the sample image and the pixel-level label thereof through a quality perception model and an anti-overfitting model to obtain a relative quality index; and adjusting parameters of the segmentation model and the quality perception model according to the segmentation loss function and the relative quality index to obtain a trained segmentation model. The invention relates to a training method and device for an image segmentation model under label fault tolerance and related equipment. According to the invention, the relative quality index is generated according to the sample image and the pixel-level label thereof. The segmentation loss function is adjusted according to the relative quality index to complete model training.Therefore, the trained segmentation model can still be ensured to have high accuracy when the training sample set has noise.

Description

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AI Technical Summary

Benefits of technology

The invention provides methods, devices, or computers program products with features like error detection capabilities (EDCs) to improve image processing systems by generating accurate estimates of errors caused during data acquisition process. This helps avoid overfitted models used in machine vision applications due to incorrect annotation values. Additionally, this technology allows users to easily verify if they have achieved desired results through experimenting alongside trained neural networks without actually having them perform any tests themselves. Overall, these technical improvements help create better imagery analysis tools such as object recognition algorithms.

Problems solved by technology

This patented technical solution involves improving the performance of learning models trained over imprecise annotated datasets without generating excessive amounts of unwanted or incorrect labelling during testing due to imperfections such as uneven gray level values caused by factors like ambient light conditions.

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