Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

Yan-Ting Liu,Rung-Ching Chen,Christine Dewi,Hui Yu

doi:10.3390/app11072913

Abstract

A synthetic image is a critical issue for computer vision. Traffic sign images synthesized from standard models are commonly used to build computer recognition algorithms for acquiring more knowledge on various and low-cost research issues. Convolutional Neural Network (CNN) achieves excellent detection and recognition of traffic signs with sufficient annotated training data. The consistency of the entire vision system is dependent on neural networks. However, locating traffic sign datasets from most countries in the world is complicated. This work uses various generative adversarial networks (GAN) models to construct intricate images, such as Least Squares Generative Adversarial Networks (LSGAN), Deep Convolutional Generative Adversarial Networks (DCGAN), and Wasserstein Generative Adversarial Networks (WGAN). This paper also discusses, in particular, the quality of the images produced by various GANs with different parameters. For processing, we use a picture with a specific number and scale. The Structural Similarity Index (SSIM) and Mean Squared Error (MSE) will be used to measure image consistency. Between the generated image and the corresponding real image, the SSIM values will be compared. As a result, the images display a strong similarity to the real image when using more training images. LSGAN outperformed other GAN models in the experiment with maximum SSIM values achieved using 200 images as inputs, 2000 epochs, and size 32 × 32.

Highlights

Neural networks with more layers have been implemented in the latest development in deep learning [1]
We only focus on Taiwan prohibitory signs that consist of no entry images (Class T1), no stopping images (Class T2), no parking images (Class T3), and speed limit images (Class T4), see Table 1
(4) The results show the better performance of Deep Convolutional Generative Adversarial Networks (DCGAN) and confirm the capability of the generative adversarial networks (GAN) structure in generating samples

Summary

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. An effective way to synthesize images is to increase the training collection which will improve image recognition accuracy. 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. A few datasets of traffic signs have been shown: German Traffic Sign Data Set (GTSRB) [5], Chinese Traffic

Methods

Results

Discussion

Conclusion