Traffic Sign Detection and Recognition Using Adaptive Threshold Segmentation with Fuzzy Neural Network Classification

Alturki, Saleh M.

doi:10.1109/isncc.2018.8531070

Cited by 9 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the localization of ROIs, classification techniques are employed to determine the content of the detected traffic signs [1]. Understanding the traffic rule enforced by the sign is achieved by reading the inner part of the detected traffic sign using a classifier method.…”

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

“…The detailed information regarding the publicly available databases are summarized Table 2. According to [1,9], the first and most widely used dataset is the German traffic sign dataset, which has two datasets: The German Traffic Signs Detection Benchmark (GTSDB) [10] and German Traffic Signs Recognition Benchmark (GTSRB) [11]. This dataset collects three important categories of road signs (prohibitory, danger and mandatory) from various traffic scenes.…”

Section: Traffic Sign Databasementioning

confidence: 99%

“…In all countries of the world, the important information about the road limitation and condition is presented to drivers as visual signals, such as traffic signs and traffic lanes. Traffic signs are an important part of road infrastructure to provide information about the current state of the road, restrictions, prohibitions, warnings, and other helpful information for navigation [1,2]. This information is encoded in the traffic signs visual traits: Shape, color and pictogram [1].…”

Section: Introductionmentioning

confidence: 99%

“…Traffic signs are an important part of road infrastructure to provide information about the current state of the road, restrictions, prohibitions, warnings, and other helpful information for navigation [1,2]. This information is encoded in the traffic signs visual traits: Shape, color and pictogram [1]. Disregarding or failing to notice these traffic signs may directly or indirectly contribute to a traffic accident.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

130

View full text Add to dashboard Cite

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

show abstract

Section: Traffic Sign Detection Tracking and Classification Methodsmentioning

confidence: 99%

Section: Traffic Sign Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Wali

Abdullah

Hannan

et al. 2019

Sensors

130

View full text Add to dashboard Cite

show abstract

“…In all countries of the world, important information about road boundaries and conditions is presented to drivers as visual signals, such as traffic signs and traffic lanes. Traffic signs are an important part of road infrastructure to provide information about the current state of the road, prohibitions, warnings, and other useful information for navigation [1,2]. In poor traffic conditions, drivers may inadvertently or deliberately not pay attention to traffic signs [3].…”

Section: Introductionmentioning

confidence: 99%

Visibilty Analysis Model to Traffic Light Using Digital Human Modeling

Sudarma¹

2022

EOIJ

View full text Add to dashboard Cite

Traffic lights signalling equipment at crossroads are very important. To regulate the procedure for placing traffic lights signalling equipment in Indonesia, the Minister of Transportation of the Republic of Indonesia Regulation Number PM 49 of 2014 regarding Traffic signal system is issued. The regulation above does not regulate how far the minimum visibility from the driver to the traffic light is. The limited space in the queue became a problem related to the views of drivers. Researchers conducted a visibility analysis using Digital Human Modelling (DHM) Tecnomatix Jack. Input variable is the type of vehicle, namely motorcycles, sedans and jeeps and the measured output is the distance from traffic light to the vehicle. A traffic light survey was conducted in Denpasar City, Bali, Indonesia. The anthropometric data of the drivers used Indonesian men, around 50% percentiles with ages ranging from 17 to 47 years from the anthropometric data of Indonesians. The testing of waiting distance is using motorcycles, jeeps, and sedans. The traffic lights used are curved type with the visibility of side lights and top lights. The results obtained that the optimal distance for the left side of the traffic light for motorcycles, jeeps, and sedans are 6.57m, 6.77m, and 6.92m, respectively and side lights up to 11.9 m, 7.64 m and 8.74 m. From the survey results, several traffic lights in Denpasar City still do not meet the minimum required distance and some queues require additional lights that are placed in front of the driver so that they can tackle the limited queuing space.

show abstract

A Deep Learning-Based Residual Network Model for Traffic Sign Detection and Classification

Devi

Subalalitha

2021

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Traffic Sign Detection and Recognition Using Adaptive Threshold Segmentation with Fuzzy Neural Network Classification

Cited by 9 publications

References 19 publications

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Visibilty Analysis Model to Traffic Light Using Digital Human Modeling

A Deep Learning-Based Residual Network Model for Traffic Sign Detection and Classification

Contact Info

Product

Resources

About