Image semantic segmentation method based on GAN network and ERFNet model

Abstract

This article addresses the problems of traditional methods in image semantic segmentation, such as insufficient segmentation of small‐scale targets and weak anti‐noise ability. A method of image semantic segmentation using a generative adversarial network (GAN) combined with ERFNet model is proposed. First, the asymmetric residual module (ARM) and weak bottleneck module are used to improve the ERFNet network model. Moreover, dilated convolution is used to reduce information loss and improve the performance of small target image semantic segmentation. Then, a U‐shaped network is used to improve the generator of GAN to avoid low‐level information sharing. In addition, the residual module is introduced into convolution layer to realise the dynamic adjustment of generator weight. Finally, the improved ERFNet model is used as the input of the generator to output the segmented image. It is input to the discriminator together with the label for judgment, which further improves the performance of image semantic segmentation. The proposed method is demonstrated experimentally based on the PyTorch platform. The results show that mean pixel accuracy and mean intersection over the union of the proposed method on the CamVid and Cityscapes datasets are higher than those of other comparisons. In addition, the execution time is short, and the overall image semantic segmentation performance is relatively ideal.

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“…More than www.ijacsa.thesai.org 70% of IoU was obtained on the Cityscapes dataset. In order to overcome the issues of limited anti-noise ability and inadequate segmentation of small-scale objects, Dong et al [36] proposed an approach using a generative adversarial network (GAN [37]) in conjunction with an ERFNet model. While these approaches have yielded good experimental results in the drivable area detection domain, they do not address the problem of poor long-range information reliance due to convolutional kernel restrictions.…”

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