APPLICATION OF GENERATIVE ADVERSARIAL NETWORK IN SEMANTIC SEGMENTATION

In order to improve the accuracy of image segmentation without changing the structure of original semantic segmentation models, an approach to train semantic segmentation models by using generative adversarial network (SS-GAN) is proposed. Using adversarial network to distinguish the source of segmented images, the model can learn the high-order relationship between pixels to enhance the spatial continuity of pixels in the segmented image. There are three aspects related to the work: constructing the generative model of fully convolutional network (FCN) structure by using VGG, and segment image preliminarily; constructing the adversarial model and training it by combining the original images, fake segmented images and real segmented images; modifying the loss function, adding the anti-loss to assist segmentation model training, encouraging generative network to learn the inter-pixel relationship independently. Experiments on PASCAL VOC and Cityscapes datasets show that the proposed method achieves better performance than the existing advanced methods, and improves IoU by 1.56% and 1.93%, respectively.