Image semantic segmentation method based on confrontation training

Abstract

The invention belongs to the technical field of computer vision, discloses an image semantic segmentation method based on confrontation training and is used for solving a problem that the existing semantic segmentation method cannot achieve meaningful fusion of global information and local information of images and cannot perform learning on high-order potential energy in the images. According tothe invention, a loss function of the whole network is defined based on a confrontation training network to serve as a confrontation network of a general function approximator, thereby not only beingcapable of learning how to combine different levels of information, but also being capable of forcing the generated network to learn information such as single points, paring and high-order potentialenergy in a segmented picture, achieving organic integration of local features and global features of the image and acquiring a segmentation image with the effect being more authentic; and meanwhile,the layer-by-layer training method avoids the complex network parameter initialization process, and enabling the whole network to use a method of random initialization.

Description

technical field [0001] The invention belongs to the technical field of computer vision and relates to image semantic segmentation and confrontation training, in particular to an image semantic segmentation method based on confrontation training. Background technique [0002] With the development and popularization of artificial intelligence, the important position of image semantic segmentation in the field of computer vision has become increasingly prominent. Many applications require accurate and efficient segmentation techniques, such as autonomous driving, indoor navigation, human-computer interaction, and more. In the past five years, deep learning methods have achieved great success in the field of computer vision, and various network structures have been proposed to solve different problems in this field, such as image classification and positioning. However, the most successful network structure convolutional neural network (Convolutional Neural Network, CNN) in the...

AI Technical Summary

Problems solved by technology

However, the most successful network structure convolutional neural network (Convolutional Neural Network, CNN) in the field of computer vision has a major disadvantage for image semantic segmentation: due to the large number of maximum pooling layers stacked in the network structure, CNN finally obtains The feature is the information of the whole picture, while ignoring the local features of the picture, such as the edge and position of the object in the picture

[0005] (1) The cross-layer connection is too simple for the fusion of different layers of information, and cannot realize the meaningful fusion of global information and local information of the image

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