A semi-supervised image instance segmentation method based on stepwise adversarial learning

Abstract

The invention discloses a semi-supervised image instance segmentation method based on stepwise adversarial learning. The semi-supervised image instance segmentation method comprises the steps of 1, constructing Mask R-CNN instance segmentation model; 2, Training FPN in a Mask R-CNN Based on DCGAN; 3, adopting labeled data to perform preliminary training on other modules in the Mask R-CNN; 4, constructing a discriminant convolutional network, and forming an adversarial learning network with the Mask R-CNN, and optimizing parameters of the adversarial learning network through adversarial training; 5, feeding back the output of the discriminative convolutional network to Mask R-CNN for retraining the instance segmentation model; And 6, segmenting the to-be-segmented image by using the instance segmentation model. According to the method, the sample set only partially labeled with the image is used for model training, the workload of sample processing is reduced, and a segmentation model with high precision can be obtained.

Description

technical field [0001] The invention relates to the field of image instance segmentation, in particular to an image semi-supervised instance segmentation method based on step-by-step confrontation learning. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. Deep learning technology automatically learns more effective image feature expression from massive data, and is successfully used for image detection and segmentation of general objects. However, there are still huge difficulties and challenges in applying deep learning methods to image detection and segmentation for specific tasks. Challenge: Existing target instance segmentation methods require that all training instances must be labeled with a segmentation mask, making annotating new categories prohibitively expensive. The pre...

AI Technical Summary

Problems solved by technology

Therefore, for the problem of semi-supervised image instance segmentation, there are still great technical difficulties and challenges

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