scholarly journals Classification of traffic signal system anomalies for environment tests of autonomous vehicles

2018 ◽  
Vol 19 (19) ◽  
pp. 43-47 ◽  
Author(s):  
Henrietta Lengyel ◽  
Zsolt Szalay
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Advantages And Disadvantages ◽  
Sign Recognition ◽  
The Future ◽  
Computer Identification ◽  
Further Development

Abstract In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.

Deep learning–based traffic sign recognition for unmanned autonomous vehicles

2018 ◽  
Vol 232 (5) ◽  
pp. 497-505 ◽  
Author(s):  
Di Zang ◽  
Zhihua Wei ◽  
Maomao Bao ◽  
Jiujun Cheng ◽  
Dongdong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
The Time Domain

Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network–based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network–based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.

scholarly journals CNN Based Approach for Traffic Sign Recognition System

2021 ◽  
Vol 11 (1) ◽  
pp. 23-33
Author(s):  
Karan Singh ◽  
Nikita Malik
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Real Life ◽  
Recognition System ◽  
Traffic Sign Recognition ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition

Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehicles.

scholarly journals Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning

2021 ◽  
pp. 215-219
Author(s):  
Manjiri Bichkar ◽  
Suyasha Bobhate ◽  
Prof. Sonal Chaudhari
Keyword(s):  
Classification Model ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Electric Cars ◽  
Blob Detection ◽  
Sign Recognition ◽  
Testing Dataset ◽  
Self Driving Cars ◽  
Traffic Sign Classification

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

Traffic Signs Recognition Based on Affine Invariant Hu's Moment Features

2013 ◽  
Vol 321-324 ◽  
pp. 945-949
Author(s):  
Min Liu ◽  
Jian Xu Mao
Keyword(s):  
Geometric Distortion ◽  
Affine Invariant ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Hu Moments ◽  
Recognition Efficiency ◽  
Moment Features ◽  
Hu Moment ◽  
Moment Feature

Traffic signs effected by shooting environment and natural environment , and varying degrees of geometric distortion. This work introduced a new method to extract the traffic signs' Hu moment features that based on affine invariant. First , according to the shape coordinates x,y of traffic sign is independent of each other before affine transformation . Then getting traffic signs only have rotating effect by ICA transformation . Finally , recognizing traffic sign by compare the Hu's moment feature. Results show this method can greatly improve the feature extraction accuracy of Hu moments and traffic sign recognition efficiency

Traffic Sign Recognition Based on SIFT Features

2010 ◽  
Vol 121-122 ◽  
pp. 596-599 ◽  
Author(s):  
Ni An Cai ◽  
Wen Zhao Liang ◽  
Shao Qiu Xu ◽  
Fang Zhen Li
Keyword(s):  
Traditional Method ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Good Ability ◽  
Sift Features

A recognition method for traffic signs based on an SIFT features is proposed to solve the problems of distortion and occlusion. SIFT features are first extracted from traffic signs and matched by using the Euclidean distance. Then the recognition is implemented based on the similarity. Experimental results show that the proposed method, superior to traditional method, can excellently recognize traffic signs with the transformation of scale, rotation, and distortion and has a good ability of anti-noise and anti-occlusion.

Study on Automation Learning System with Electronic Materials

2013 ◽  
Vol 738 ◽  
pp. 235-238
Author(s):  
Xian Zhi Tian
Keyword(s):  
Energy Saving ◽  
Electronic Materials ◽  
Learning System ◽  
Future Application ◽  
Automation System ◽  
Saving Energy ◽  
Advantages And Disadvantages ◽  
Fast Speed ◽  
The Future ◽  
Further Development

In recent years, with the development of mechanical automation,automation system had been developed quickly, and it covers different fields in different areas. As for automation learning system, it had been developed by some researchers and learners for its fast speed and energy-saving properties. At the same time, automation learning system is also useful for trainers to develop and improve. Therefore, the author had made some investigations and tried to dig out its advantages and disadvantages, which are useful for its further development in the future application. During the study, the author also found that some electronic materials had been useful to stimulate the uses of automation learning system.From the study, the author hopes that the study will help to improve learning efficiency and saving energy.

Research on Generalization Technology of Spatial Line Vector Data

2014 ◽  
Vol 687-691 ◽  
pp. 1153-1156
Author(s):  
Shi Qing Dou ◽  
Xiao Yu Zhang
Keyword(s):  
Spatial Data ◽  
Three Dimensional ◽  
Vector Data ◽  
Advantages And Disadvantages ◽  
3D Environment ◽  
Development Direction ◽  
The Future ◽  
2D And 3D ◽  
Data Generalization

Data simplification is an important factor of the spatial data generalization, which is an effective way to improve rendering speed. This paper firstly introduces the algorithms classification of the spatial line vector data in two-dimensional environment, and then it emphatically summarizes and analyzes the advantages and disadvantages of the algorithms which can be used in the spatial line vector data simplification in the three dimensional environment. The three-dimensional Douglas-Peucker algorithm with a certain overall characteristics has wide application prospect. The simplified algorithms in 3D environment represent the development direction of the future. But at present, the existing data simplification algorithms in 3D environment are not mature enough, they all have certain advantages and disadvantages, this makes their use is limited by a certain extent. The application of these simplified algorithms in 2D and 3D is mostly on multi-resolution expression. Developing from 2D algorithm to the direction of 3D algorithm, it also lists many works and problems that need us to do and study in the future.

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