Recognition of Traffic Signs and Korean Texts on Traffic Signs Using Japanese Puzzle

2008 ◽  
Vol 12 (6) ◽  
pp. 509-515
Author(s):  
Youngsun Sohn ◽  
◽  
Ilsik Shin ◽  
Keyword(s):  
Image Processing ◽  
Processing Speed ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Black And White ◽  
Intended Object

This paper embodies a recognition system that recognizes the traffic signs and the Korean characters on the traffic signs through reverse application of a Japanese puzzle. The Japanese puzzle used in this system reveals the shape of the intended object when marked onto the mesh grids according to the (x,y) coordinate information provided by the puzzle creator. When the puzzle described above is applied to the color and the shape of the traffic sign after the separating the traffic sign image from the inputted image, the system outputs the traffic sign and its contents as text if the image is recognized as a traffic sign. With the black-and-white image processing and unneeded penciling procedure, the proposed system outperformed the existing systems at a faster processing speed and higher recognition rate.

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.

Traffic-Signs Recognition System Based on FCM and Content-Based Image Retrieval

2012 ◽  
pp. 60-72
Author(s):  
Yue Li ◽  
Wei Wang
Keyword(s):  
Image Retrieval ◽  
Recognition System ◽  
Emerging Technology ◽  
Content Based Image Retrieval ◽  
Segmentation Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Intelligent ◽  
Traffic System ◽  
Two Phases

Artificial intelligent (AI) driving is an emerging technology, freeing the driver from driving. Some techniques for automatically driving have been developed; however, most can only recognize the traffic signs in particular groups, such as triangle signs for warning, circle signs for prohibition, and so forth, but cannot tell the exact meaning of every sign. In this paper, a framework for a traffic system recognition system is proposed. This system consists of two phases. The segmentation method, fuzzy c-means (FCM), is used to detect the traffic sign, whereas the Content-Based Image Retrieval (CBIR) method is used to match traffic signs to those in a database to find the exact meaning of every detected sign.

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

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3776 ◽  
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.

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

Vestnik NSUEM ◽  
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.

scholarly journals Traffic Sign Detection Using Region And Corner Feature Extraction Method

2021 ◽  
Vol 3 (1) ◽  
pp. 21-24
Author(s):  
Hendra Maulana ◽  
Dhian Satria Yudha Kartika ◽  
Agung Mustika Riski ◽  
Afina Lina Nurlaili
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Safety Information ◽  
Extraction Methods ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Feature Extraction Method ◽  
Analysis Process ◽  
Sign Detection ◽  
Traffic Sign Detection

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

scholarly journals Real-time traffic sign detection and recognition using Raspberry Pi

2022 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Ida Syafiza Binti Md Isa ◽  
Choy Ja Yeong ◽  
Nur Latif Azyze bin Mohd Shaari Azyze
Keyword(s):  
Real Time ◽  
Video Recording ◽  
Recognition System ◽  
Road Accident ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Real Time Traffic ◽  
Driving Assistance

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

The A2iA Intercheque System: Courtesy Amount and Legal Amount Recognition for French Checks

1997 ◽  
Vol 11 (04) ◽  
pp. 505-548 ◽  
Author(s):  
S. Knerr ◽  
V. Anisimov ◽  
O. Baret ◽  
N. Gorski ◽  
D. Price ◽  
...  
Keyword(s):  
Processing Speed ◽  
Recognition Rate ◽  
Recognition System ◽  
Error Rates ◽  
Pattern Recognition Techniques ◽  
Check Reading ◽  
High Processing Speed ◽  
Bank Checks ◽  
Reading System ◽  
High Processing

We developed a check reading system, termed INTERCHEQUE, which recognizes both the legal (LAR) and the courtesy amount (CAR) on bank checks. The version presented here is designed for the recognition of French, omni-bank, omni-scriptor, handwritten bank checks, and meets industrial requirements, such as high processing speed, robustness, and extremely low error rates. We give an overview of our recognition system and discuss some of the pattern recognition techniques used. We also describe an installation which processes of the order of 70,000 checks per day. Results on a data base of about 170,000 checks show a recognition rate of about 75% for an error rate of the order of 1/10,000 checks.

scholarly journals Traffic signs recognitionusing cuckoo search algorithm and Curvelettransform with image processing methods

2019 ◽  
Vol 11 (2) ◽  
pp. 74-81
Author(s):  
Ahmed Saadi Abdullah ◽  
Majida Ali Abed ◽  
Ahmed Naser Ismael
Keyword(s):  
Image Processing ◽  
Traffic Accidents ◽  
Search Algorithm ◽  
Curvelet Transform ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Fourth Step ◽  
Processing Techniques

Compliance with traffic signs is one of the most important things to follow to avoid traffic accidents as well as compliance with traffic rules in terms of parking, speed control, and other traffic sings. Progress in different areas, such as self-propelled car manufacturing or the production of devices that help the visually impaired, require values to find a way to determine traffic signals with high precision in this research, The first step is to take a picture of the traffic sign and apply some digital image processing techniques to increase image contrast and eliminate noise in the image, the second step resize of origin image  , the third step convert color to(YCbCr, HSB) or stay on RGB, the fourth step  image is disassembled using  curvelet  transform and get coefficients , and the last step using cuckoo search algorithm to recognition sings traffics ,the MATLAB (2011b) program was used to implement the proposed algorithm . After applying this method to a set of traffic the percentage of discrimination of traffic signs was yellow 93%, green 94%, blue 94.5%, red 96%.

scholarly journals Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4021 ◽  
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Silun Peng ◽  
Feng Xiao ◽  
Shixin Song
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Intelligent Vehicles ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.

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