Feb 10, 2021 · Convolutional Neural Networks (CNNs) achieve excellent in traffic sign detection and recognition with sufficient annotated training data.
Abstract—Convolutional Neural Networks (CNNs) achieve excellent in traffic sign detection and recognition with sufficient annotated training data.
The generated images show high similarity with the actual image while using more images for training, and the highest SSIM values reached when using 200 ...
Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. When the training and testing data ...
Generate Realistic Traffic Sign Image Using Deep Convolutional Generative Adversarial Networks · Yan-Ting Liu · Rung-Ching Chen · Christine Dewi.
This study focuses on the consistency of DCGAN and WGAN images created with varied settings. We utilize an actual picture with various numbers and scales for ...
In this study, we propose a traffic data imputation framework based on generative adversarial network (TSDIGAN) encoding the time series into images. This ...
Experiments show that this method could generate more realistic traffic sign images than the conventional image synthesis method and by adding the synthesis ...
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In this project, I attempt to use the generator in the GAN architecture to create synthetic images of already collected and labeled traffic signs.
The generator aims to create fake images that resemble real ones to deceive the discriminator, while the discriminator's goal is to accurately distinguish ...