# Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
## 1 Introduction  
Neural networks with more layers have been implemented in the latest development in deep learning [1]. These neural network models are far more capable of acquiring greater preparation. Nonetheless, obtaining a correct and reliable data collection with manual labeling is often costly. In machine learning as well as computer vision, this has been a general problem. An effective way to synthesize images is to increase the training collection which will improve image recognition accuracy. Employing data augmentation for enlarging the training set in image classification has been carried out in various research [2]. Traffic sign detection (TSD) and traffic sign recognition (TSR) technology have been thoroughly researched and discussed by researchers in recent years [3, 4]. Many TSD and TSR systems consist of large quantities of training data. In recent years, a few datasets of traffic signs have been shown: German Traffic Sign Data Set (GTSRB) [5], Chinese Traffic Sign Database (TSRD), and Tsinghua-Tencent 100K (TT100K) [6]. Traffic signs are different from country to country and, in various circumstances, an interesting recommendation is to apply synthetically generated training data. The synthetic image will save time and energy for data collection [7, 8]. Synthetic training data have not yet been commonly used in the TSR sector but are worth exploring because very few datasets come from other countries, in particular from Taiwan. In this research, we focus on Taiwan's prohibitory signs. Ourmotivation arises from the current unavailability of such a Taiwan traffic sign database, image and research system. A generative adversarial network (GAN) [9] is a deep research framework of two models, a generative model and a discriminative model. Both models are instructed together. GAN has brought a lot of benefits to several specific tasks, such as images synthesis [10; 11; 12], image-to-image translation [13; 14], and image restoration [15]. Image synthesis is a fundamental problem in computer vision [16; 17; 18]. In order to obtain more diverse and low-cost training data, traffic sign images synthesized from standard templates have been widely used to train classification algorithms based on machine learning [12; 19]. [PERSON] et al. [20] proposed the Deep Convolutional Generative Adversarial Network (DCGAN) in 2016. DCGAN combines the Generative Adversarial Network (GAN) with CNN so that all GANs can obtain better and more stable training results. Other versions of GAN are Least Squares Generative Adversarial Networks (LSGAN) and Wasserstein Generative Adversarial Networks (WGAN) [21; 22]. Both can better solve the problem of instability training in GAN. Each GAN has achieved excellent results in producing synthetic imagery. Therefore, due to the lack of a current training dataset, our experiments apply DCGAN, LSGAN, and WGAN to generate synthetic images. Traffic sign images compiled from regular models are commonly used to collect additional training data with low cost and flexibility to train computer classification algorithms [19]. In this paper, DCGAN, LSGAN, and WGAN are used to generate complicated images. The synthetic image is a solution for holding a small amount of data. GAN has performed outstanding results in image data generation. Our experiment favors using synthetic images by GAN to obtain image data because this does not depend on a vast number of datasets for training. This work's main contributions can be summarized as follows: first, a synthesis of high-quality Taiwan prohibitory sign images Class (T1-T4) is obtained using various GAN models. Second, an analysis and evaluation performance of DCGAN, LSGAN, and WGAN generates a synthetic image with different epochs (1000 and 2000), numbers, and sizes (64 \(\times\) 64, and 32 \(\times\) 32). Next, we proposed an experimental setting with various GAN styles to generate a synthetic image. We then evaluate the synthetic image using SSIM and MSE. The remainder of this article is structured as follows. Section 2 covers materials and methods.