Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

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.

[1]  Jason Yosinski,et al.  Metropolis-Hastings Generative Adversarial Networks , 2018, ICML.

[2]  Jie Li,et al.  AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[3]  Jayanthi Sivaswamy,et al.  Synthesis of Optical Nerve Head Region of Fundus Image , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[4]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[5]  Seiichi Uchida,et al.  Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks , 2017, 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR).

[6]  Peter Corcoran,et al.  Smart Augmentation Learning an Optimal Data Augmentation Strategy , 2017, IEEE Access.

[7]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Jana Kosecka,et al.  Synthesizing Training Data for Object Detection in Indoor Scenes , 2017, Robotics: Science and Systems.

[9]  Degang Sun,et al.  A New Mimicking Attack by LSGAN , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).

[10]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.

[11]  Eliathamby Ambikairajah,et al.  DA-DCGAN: An Effective Methodology for DC Series Arc Fault Diagnosis in Photovoltaic Systems , 2019, IEEE Access.

[12]  Baoli Li,et al.  Traffic-Sign Detection and Classification in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Yufei Huang,et al.  Modeling EEG Data Distribution With a Wasserstein Generative Adversarial Network to Predict RSVP Events , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[15]  Yu Du,et al.  DCGAN Based Data Generation for Process Monitoring , 2019, 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS).

[16]  C. C. Pain,et al.  Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network , 2020, Computer Methods in Applied Mechanics and Engineering.

[17]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Rung-Ching Chen,et al.  Taiwan Stop Sign Recognition with Customize Anchor , 2020, ICCMS.

[20]  Bogdan J. Matuszewski,et al.  CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN , 2020, MIUA.

[21]  Mohan M. Trivedi,et al.  Learning to detect traffic signs: Comparative evaluation of synthetic and real-world datasets , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[22]  Han Zhang,et al.  Self-Attention Generative Adversarial Networks , 2018, ICML.

[23]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Mei Yu,et al.  Weighted-to-Spherically-Uniform SSIM Objective Quality Evaluation for Panoramic Video , 2018, 2018 14th IEEE International Conference on Signal Processing (ICSP).

[25]  Xiaogang Wang,et al.  StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Johannes Stallkamp,et al.  The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.

[27]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[28]  Guo-Jun Qi,et al.  Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities , 2017, International Journal of Computer Vision.

[29]  D. Liang,et al.  A 3D attention residual encoder–decoder least-square GAN for low-count PET denoising , 2020 .

[30]  Leyuan Fang,et al.  Retinal optical coherence tomography image classification with label smoothing generative adversarial network , 2020, Neurocomputing.

[31]  Qian Ai,et al.  Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network , 2020 .

[32]  Alberto Diaspro,et al.  The 2015 super-resolution microscopy roadmap , 2015, Journal of Physics D: Applied Physics.

[33]  Morteza Mardani,et al.  Deep Generative Adversarial Neural Networks for Compressive Sensing MRI , 2019, IEEE Transactions on Medical Imaging.

[34]  Rung Ching Chen,et al.  Weight analysis for various prohibitory sign detection and recognition using deep learning , 2020, Multimedia Tools and Applications.

[35]  Wei Fang,et al.  Gesture Recognition Based on CNN and DCGAN for Calculation and Text Output , 2019, IEEE Access.

[36]  RAOul M. S. JOeMAi,et al.  Assessment of structural similarity in CT using filtered backprojection and iterative reconstruction: a phantom study with 3D printed lung vessels. , 2017, The British journal of radiology.

[37]  Zhen Wang,et al.  On the Effectiveness of Least Squares Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Bin Fan,et al.  Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network , 2018, IEEE Transactions on Intelligent Transportation Systems.

[39]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[40]  Rung-Ching Chen,et al.  Selecting critical features for data classification based on machine learning methods , 2020, Journal of Big Data.

[41]  Heung-Kyu Lee,et al.  Median Filtered Image Restoration and Anti-Forensics Using Adversarial Networks , 2018, IEEE Signal Processing Letters.

[42]  Rung-Ching Chen,et al.  Evaluation of Robust Spatial Pyramid Pooling Based on Convolutional Neural Network for Traffic Sign Recognition System , 2020, Electronics.

[43]  Zhenzhong Chen,et al.  Thermal to Visible Facial Image Translation Using Generative Adversarial Networks , 2018, IEEE Signal Processing Letters.

[44]  Xiaoyi Jiang,et al.  Deep Learning for Traffic Sign Recognition Based on Spatial Pyramid Pooling with Scale Analysis , 2020 .

[45]  Integrating gesture control board and image recognition for gesture recognition based on deep learning , 2020 .

[46]  Shahrokh Valaee,et al.  Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[47]  Martial Hebert,et al.  Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[48]  K. P. Soman,et al.  Deep Learning Approach for Intelligent Intrusion Detection System , 2019, IEEE Access.

[49]  Chuang Wang,et al.  Study of Restrained Network Structures for Wasserstein Generative Adversarial Networks (WGANs) on Numeric Data Augmentation , 2020, IEEE Access.

[50]  Miao Li,et al.  The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow , 2019, Physica A: Statistical Mechanics and its Applications.

[51]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[52]  Dumitru Erhan,et al.  Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[53]  Zhou Yu,et al.  Multimodal Transformer With Multi-View Visual Representation for Image Captioning , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[54]  Nanfeng Xiao,et al.  Improved Boundary Equilibrium Generative Adversarial Networks , 2018, IEEE Access.

[55]  Chang Ouk Kim,et al.  A run-to-run controller for a chemical mechanical planarization process using least squares generative adversarial networks , 2020, Journal of Intelligent Manufacturing.

[56]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Hendry,et al.  Similar Music Instrument Detection via Deep Convolution YOLO-Generative Adversarial Network , 2019, 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST).