Traffic Sign Recognition in Autonomous Vehicles Using Edge Detection

2017 ◽  
Author(s):  
Harish Vishwanathan ◽  
Diane L. Peters ◽  
James Z. Zhang
Keyword(s):  
Edge Detection ◽  
Autonomous Vehicles ◽  
Human Error ◽  
Linear Prediction ◽  
New Technology ◽  
Night Vision ◽  
Detection Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Stop Sign

Autonomous vehicles have the potential to improve safety by eliminating human error in driving, as well as providing mobility to those who cannot safely drive. Such vehicles do require new technology to monitor their environment and ensure that they are operating safely. One such technology that will be necessary is the ability of the vehicle to recognize traffic situations and traffic signs. This can be accomplished by an appropriate implementation of edge detection methods. In this paper, we compare three different edge detection methods: Canny method, Sobel method and Zhang method. This comparison was conducted on both still pictures and on a video. When analyzing the video, which was taken on a clear day with an undamaged and clearly visible stop sign, all three methods performed equally well; the time at which the stop sign was identified, based on the edge map, was the same. The purpose of this comparison is to evaluate the performance of each of the three methods, in the context of the problem of identifying traffic signs. The methods are compared on still images of a stop sign under various conditions, in addition to the single video comparison. Based on the still image comparison, we conclude that Zhang’s method (linear prediction) generates the best edge map, particularly when the images include snow, ice, rain or other factors and even at night vision.

Traffic Sign Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4284-4288
Author(s):  
Janhavi Anand Chavan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Driver Assistance System ◽  
Processing System ◽  
Driver Assistance ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Arduous Task ◽  
Key Factor

Detecting and classifying traffic signs has been of uttermost importance ever since the emergence of Advanced driver assistance system and autonomous vehicles. Traffic signs are one of the decisive factor for traffic supervision. Because of complexities in driving nature it is difficult to detect the traffic signs and classify them because of partial occlusion and their small size. Different methods were contemplated for the same but there is a need to enhance them to fulfill the necessities of autonomous vehicles. Detection and recognition of small objects with the help of image processing system was an arduous task. Deep learning algorithms have been a key factor for recognition of traffic signs. Convolution Neural Networks are used for the mechanism. These method aids to get the accuracy in spite of the disturbances. Training of neural networks is implemented with the help of Tensorflow and Keras library.

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.

Comparative Analysis of Edge Detection Methods and its Implementation using UTLP Kit

2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Edge Detection ◽  
Detection Methods

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

2021 ◽  
Vol 11 (8) ◽  
pp. 3666
Author(s):  
Zoltán Fazekas ◽  
László Gerencsér ◽  
Péter Gáspár
Keyword(s):  
Change Detection ◽  
Traffic Safety ◽  
Recognition System ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safety Risk ◽  
Urban Road ◽  
Environment Type ◽  
Cusum Method

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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