Vol.:(0123456789) 1 3 Journal of Ambient Intelligence and Humanized Computing https://doi.org/10.1007/s12652-021-03261-2 ORIGINAL RESEARCH An edge detection–based eGAN model for connectivity in ambient intelligence environments Cho Youn Lee 1  · Jin Gon Shon 1  · Ji Su Park 2 Received: 10 January 2021 / Accepted: 29 March 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract In generative adversarial networks (GANs), a generator network and discriminator network compete in deep learning tasks to generate real images. To reduce the diference 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. Keywords Generative adversarial networks · Edge detection · EGAN · Threshold · Smart cities · Ambient intelligence 1 Introduction In generative adversarial networks (GANs), a generator network and discriminator network compete hostilely in the feld of deep learning and generate image data similar to real-world data. Initially, GAN becomes unstable as it changes to various images in the learning process, which creates realistic images. Theoretically, convergence is guaranteed, but when minimization and maximization are applied while learning the distribution of training data, the theoretical assumption is broken, resulting in unstable results. Because learning is unstable, images are repeatedly generated using the generator and discriminator; this leads to excessive crushing of the outline and poor image quality. To improve this situation, various types of GAN models have been actively examined and their utilization has increased as a model that generates images in the feld of deep learning. Because numerous images are composed of linear and non- linear images, it is important to use edge detection methods to create realistic similar images. GAN-based edge detection is being actively studied to ensure accurate image data and improve the super resolution (SR). GAN uses a loss function to reduce the data error between the generated image and actual image. In this study, an edge GAN (eGAN) model using edge detection is proposed to reduce the loss error. Edge detection is used to obtain useful information in com- puter vision systems (Goodfellow 2014). The edge detection process simplifes the analysis of images while signifcantly reducing the amount of data to be processed; moreover, it reveals useful structural information about object boundaries. The frst and most obvious criterion * Ji Su Park jisupark@jj.ac.kr Cho Youn Lee qecche77@knou.ac.kr Jin Gon Shon jgshon@knou.ac.kr 1 Department of Computer Science, Graduate School, Korea National Open University, Seoul, South Korea 2 Department of Computer Science and Engineering, Jeonju University, Jeonju, South Korea