Traffic Sign Classification by Image Preprocessing and Neural Networks

2007 ◽  
pp. 741-748
Author(s):  
R. Vicen-Bueno ◽  
A. García-González ◽  
E. Torijano-Gordo ◽  
R. Gil-Pita ◽  
M. Rosa-Zurera
Keyword(s):  
Neural Networks ◽  
Traffic Sign ◽  
Image Preprocessing ◽  
Traffic Sign Classification

Convolution Neural Networks for Traffic Sign Classification

2019 ◽  
Author(s):  
Rajashekar A ◽  
Shruti Hegdekar ◽  
Dikpal Shrestha ◽  
Prabin Nepal ◽  
Sujanb Neupane
Keyword(s):  
Neural Networks ◽  
Traffic Sign ◽  
Convolution Neural Networks ◽  
Traffic Sign Classification

scholarly journals Traffic Sign Classification Using Convolutional Neural Networks and Computer Vision

2021 ◽  
Vol 12 (3) ◽  
pp. 4244-4250
Author(s):  
Anuraag Velamati Et.al
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Human Beings ◽  
Traffic Sign ◽  
The World ◽  
Traffic Sign Classification

The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.

scholarly journals IMAGE SYNTHESIS WITH NEURAL NETWORKS FOR TRAFFIC SIGN CLASSIFICATION

2018 ◽  
Vol 42 (1) ◽  
pp. 105-112 ◽  
Author(s):  
V. I. Shakhuro ◽  
A. S. Konushin
Keyword(s):  
Neural Networks ◽  
Synthetic Data ◽  
Real Data ◽  
Image Synthesis ◽  
Image Generation ◽  
Wasserstein Metric ◽  
Traffic Sign ◽  
Baseline Method ◽  
Training Samples ◽  
Traffic Sign Classification

In this work, we research the applicability of generative adversarial neural networks for generating training samples for a traffic sign classification task. We consider generative neural networks trained using the Wasserstein metric. As a baseline method for comparison, we take image generation based on traffic sign icons. Experimental evaluation of the classifiers based on convolutional neural networks is conducted on real data, two types of synthetic data, and a combination of real and synthetic data. The experiments show that modern generative neural networks are capable of generating realistic training samples for traffic sign classification that outperform methods for generating images with icons, but are still slightly worse than real images for classifier training.

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

Sign in / Sign up

Export Citation Format

Share Document