Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study

Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for more than a decade now, the available ones differ in hardware and software solutions they use, as well as in quantity and typology of signs they recognize. The aim of this study is to determine whether differences between detection and readability accuracy of market-ready TSRS exist and to what extent, as well as how different levels of “graphical changes” on the signs affect their accuracy. For this purpose, signs (“speed limit” and “prohibition of overtaking”) were placed on a test field and 17 vehicles from 14 different car brands underwent testing. Overall, the results showed that sign detection and readability by TSRS differ between car brands and that even small changes in the design of signs can drastically affect TSRS accuracy. Even in a controlled environment where no sign has been altered, there has been a 5% margin of misread signs.

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An Approach of Traffic Sign Recognition Algorithm on MATLAB

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.3980 ◽

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.

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A Review and Comparative Analysis of Recent Advancements in Traffic Sign Detection and Recognition Techniques

SAMRIDDHI A Journal of Physical Sciences Engineering and Technology ◽

10.18090/samriddhi.v2i1.1594 ◽

2015 ◽

Vol 2 (1) ◽

Author(s):

Khyati Chourasia ◽

Jitendra N. Chourasia

Keyword(s):

Template Matching ◽

Safety Information ◽

Recognition System ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Fuzzy Classifier ◽

Sign Recognition ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

This paper presents a comprehensive study of the automatic detection and recognition of traffic sign. The object of this review is to reduce the search for quality Traffic sign recognition system and to indicate the potential regions for increasing the efficiency, accuracy and speed of the system. The traffic sign carry the very important and valuable safety information through the peculiar characteristics. Different categories of traffic sign with their characteristics are presented. The practical difficulty that arises in actual time traffic sign is summarized. It describes also the techniques used for the detection, recognition and classification of the traffic signs. The traffic sign detection using color and shape detection are most commonly used. Some authors also used adaboost detector and decision tree method for detection. Most of the researcher used different type of Neural Network for recognition and classification. Some of the authors used fuzzy classifier and genetic algorithm. Template matching and model based method is also used for classification. A lot of improvements are still required for development efficient, fast, robustness traffic sign recognition system.

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New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

Sensors ◽

10.3390/s18113776 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3776 ◽

Cited By ~ 2

Author(s):

Jameel Khan ◽

Donghoon Yeo ◽

Hyunchul Shin

Keyword(s):

Recognition Performance ◽

Recognition System ◽

Tone Mapping ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Traffic Signs ◽

Sign Recognition ◽

Sign Detection ◽

Illumination Preprocessing ◽

Traffic Sign Detection

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

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Modification of the Pre-Processing Stage of a Traffi Sign Recognition System Taking into Account the Characteristics of the Subject Area

Vestnik NSUEM ◽

10.34020/2073-6495-2020-2-235-249 ◽

2020 ◽

pp. 235-249

Author(s):

S. Yu. Pchelintsev

Keyword(s):

Processing Speed ◽

Recognition System ◽

Subject Area ◽

Traffic Sign Recognition ◽

Traffic Sign ◽

Road Signs ◽

Sign Recognition ◽

The Subject ◽

Recognition Systems ◽

High Level

Traffic sign recognition systems require a high level of responsiveness and accuracy with limited use of computing resources. The process of image pre-processing precedes the process of directly recognizing images, therefore, the recognition results depend on its effectiveness. When conducting pre-processing, it is important to take into account the features of the subject area, within which recognition is performed. The article discusses the process of pre-processing and preparing images in the context of creating a system for recognizing road signs. The main problems that arise during the operation of such a system are identified. Their solutions are proposed. Own combination of these solutions allowed us to create a new system for recognizing road signs, which gives a gain in processing speed by cutting off images of no interest before entering the classifier, and also taking into account the peculiarities of operation in an urban environment – more difficult conditions compared with recognition of road signs on tracks or on artificially created training grounds.

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