Detection and recognition of text traffic signs above the road

2021 ◽  
Vol 35 (2) ◽  
pp. 69
Author(s):  
Wei Sun ◽  
Yangtao Du ◽  
Xu Zhang ◽  
Guoce Zhang
Keyword(s):  
Traffic Signs ◽  
The Road ◽  
Detection And Recognition

Detection and recognition of text traffic signs above the road

2021 ◽  
Vol 35 (2) ◽  
pp. 69
Author(s):  
Yangtao Du ◽  
Xu Zhang ◽  
Guoce Zhang ◽  
Wei Sun
Keyword(s):  
Traffic Signs ◽  
The Road ◽  
Detection And Recognition

scholarly journals Traffic Sign Detection and Recognition using Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 1059-1063
Author(s):  
Mr. Mohammad Shabbir Sheikh
Keyword(s):  
Gabor Filter ◽  
Learning Technology ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Detection And Identification ◽  
Sign Detection ◽  
On The Road ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

scholarly journals Traffic Sign Detection Based on Convolutional Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 2926-2934
Author(s):  
Arjun Dileep
Keyword(s):  
Recognition Rate ◽  
Vital Role ◽  
Motive Force ◽  
Image Process ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Help Systems ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

A Precise Positioning System Based on DGPS and GIS for Automatic Driving Vehicle

2013 ◽  
Vol 416-417 ◽  
pp. 776-780
Author(s):  
Jian Guo Zhang ◽  
Hong Jian Li ◽  
Bin Liu
Keyword(s):  
Data Base ◽  
Intelligent Vehicle ◽  
Positioning System ◽  
Accurate Data ◽  
Traffic Signs ◽  
Precise Positioning ◽  
Traffic Lights ◽  
The Road ◽  
Automatic Driving ◽  
Position Data

A precise positioning technology was introduced in this paper and A precise positioning system has been built. The DGPS technology was introduced to acquire accurate data of the roads and traffic facilities in the system and a data-base has been built. The data-base was applied to GIS platform and a centimeter-level accurate road model was established. In addition, DGPS technology was adopted to achieve centimeter-level position. The position data coupling with accurate road model and the locating point would be indicated instantaneously. The distance between the locating point and the road boundaries, traffic signs lines, traffic lights, etc. could be indicated in the map and also the speed, acceleration, etc. of the locating point. This study provided positioning and navigation technology for the automatic driving technology of the intelligent vehicle.

scholarly journals Analisis Pengaruh Geometrik Dan Kelengkapan Rambu Lalu Lintas Terhadap Kecelakaan (Studi Kasus : Tanjakan Kethekan Kec. Jambu, Ruas Jalan Ambarawa – Magelang Km. 46+000 S/D 46+750)

2019 ◽  
Vol 24 (1) ◽  
pp. 60
Author(s):  
Rizqi Rangga Perdana ◽  
Yeremia Kristian Adi Permata ◽  
Siti Latifah ◽  
Sukoyo Sukoyo ◽  
Wasino Wasino
Keyword(s):  
Traffic Accidents ◽  
Geometric Analysis ◽  
Black Spot ◽  
Vehicle Speed ◽  
Traffic Signs ◽  
The Road ◽  
Central Java ◽  
Geometric Conditions ◽  
Arterial Road ◽  
The Relationship

<p class="Default"><em>Ambarawa – Magelang main road is an arterial road which used by drivers who came from Central Java headed to Yogyakarta. The Road Segment is considered to have frequent traffic accidents. The study aims to analyze the relationship between geometric aspects and the occurrence of accidents, compare actual speed with plan speed, and analyzing the completeness of the traffic signs on this section to identify the causes of accidents that occurred at that location</em>. <em>The stages of analysis is by doing the geometric calculation of the road and compared with the requirements in the regulation then identifying whether there is a relationship between accidents that occur with geometric conditions, vehicle speed, and the signs completeness. Identification and results of geometric analysis is known that Black Spot are on bend road I (km 46+300) there were 22 accidents due to breaking road markings, 6 accidents due to extreme bend, and 4 accidents due to road slackness throughout 2012 – 2018. On the bend road II (km 46+440) there were 5 accidents due to breaking road markings and on the bend road III (km 46+520) there were 3 accidents due to road slackness. with the result that there is a relationship between geometric conditions with accidents that occur and after being analyzed the completeness of the traffic signs is still lacking and needs to be completed.</em></p>

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