scholarly journals Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 583
Author(s):  
Gabriel Villalonga ◽  
Joost Van de Weijer ◽  
Antonio M. López
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sign Recognition ◽  
Short Period ◽  
Synthetic Images

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio ∼ 1 / 4 for new/known classes; even for more challenging ratios such as ∼ 4 / 1 , the results are also very positive.

scholarly journals Road images augmentation with synthetic traffic signs using neural networks

2021 ◽  
Vol 5 (45) ◽  
pp. 736-748
Author(s):  
A.S. Konushin ◽  
B.V. Faizov ◽  
V.I. Shakhuro
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Training Data ◽  
Traffic Sign ◽  
Generative Adversarial Network ◽  
Traffic Signs ◽  
Adversarial Network ◽  
Sign Recognition ◽  
Variational Autoencoder ◽  
Sign Detection

Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector.

scholarly journals Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ji Eun Park ◽  
Dain Eun ◽  
Ho Sung Kim ◽  
Da Hyun Lee ◽  
Ryoung Woo Jang ◽  
...  
Keyword(s):  
Isocitrate Dehydrogenase ◽  
Synthetic Data ◽  
Diagnostic Model ◽  
Generative Adversarial Network ◽  
Classification Rate ◽  
Mutant Type ◽  
Clinical Model ◽  
The Real ◽  
Adversarial Network ◽  
Synthetic Images

AbstractGenerative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as a rare disease of isocitrate dehydrogenase (IDH)-mutant glioblastomas. GAN was initially trained on 500 normal brains and 110 IDH-mutant high-grade astocytomas, and paired contrast-enhanced T1-weighted and FLAIR MRI data were generated. Diagnostic models were developed from real IDH-wild type (n = 80) with real IDH-mutant glioblastomas (n = 38), or with synthetic IDH-mutant glioblastomas, or augmented by adding both real and synthetic IDH-mutant glioblastomas. Turing tests showed synthetic data showed reality (classification rate of 55%). Both the real and synthetic data showed that a more frontal or insular location (odds ratio [OR] 1.34 vs. 1.52; P = 0.04) and distinct non-enhancing tumor margins (OR 2.68 vs. 3.88; P < 0.001), which become significant predictors of IDH-mutation. In an independent validation set, diagnostic accuracy was higher for the augmented model (90.9% [40/44] and 93.2% [41/44] for each reader, respectively) than for the real model (84.1% [37/44] and 86.4% [38/44] for each reader, respectively). The GAN-based synthetic images yield morphologically variable, realistic-seeming IDH-mutant glioblastomas. GAN will be useful to create a realistic training set in terms of morphologic variations and quality, thereby improving diagnostic performance in a clinical model.

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Traffic sign recognition system for inventory purposes

2008 ◽  
Author(s):  
S. Maldonado-Bascon ◽  
S. Lafuente-Arroyo ◽  
P. Siegmann ◽  
H. Gomez-Moreno ◽  
F.J. Acevedo-Rodriguez
Keyword(s):  
Recognition System ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition
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