An edge detection–based eGAN model for connectivity in ambient intelligence environments

In generative adversarial networks (GANs), a generator network and discriminator network compete in deep learning tasks to generate real images. To reduce the difference between the generated image and actual image, an edge GAN (eGAN) model using edge detection was proposed. This eGAN model can utilize ambient intelligence, a human-centered technology that includes IoT, smart cities, and autonomous driving. Ambient intelligence is essential for the interconnection between humans and objects. The eGAN model was used to make this connectivity more accurate and reliable. Edge detection is an edge feature that extracts the boundaries of an image and generate images in a fast manner; however, because its threshold is arbitrarily set, the connectivity may be unstable. To solve this problem and improve the performance of the eGAN model, we analyzed various GAN models and edge detection methods and proposed a new edge detection technology using threshold settings. This edge detection method sets the threshold value for images, thereby increasing the accuracy of edge connection and reducing the loss error between the image generated by the eGAN model and actual image. To evaluate the performance of the eGAN model, the error between the generated image and actual image was compared by applying the GAN and eGAN models to the same image dataset. Consequently, it was found that the performance of the eGAN model improved by 21% in comparison to the existing GAN model.