Traffic sign detection and recognition using fuzzy segmentation approach and artificial neural network classifier respectively

Abedin, Zainal; Dhar, Prashengit; Hossenand, Muhammad Kamal; Deb, Kaushik

doi:10.1109/ecace.2017.7912960

Cited by 18 publications

(14 citation statements)

References 13 publications

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“…When using color information in road sign detection methods, change in lighting, strong illumination and adverse weather conditions can result in problematic changes. These problems were overcome by using Color models such as HSV [14], [9], [11], [25], [26], YUV [13], YCbCr [18,38,40] and CIECAM97 [15]. Although color based approaches produce faster results in most of the cases it reduces the detection rate in the cluttered environment.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

“…The most widely used supervised learning approaches for traffic sign recognition are ANN Ref. [6,25,26,28,36,38,40],SVM Ref. [12,21,23], Random Forest Ref.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

“…Ref [26] uses fuzzy logic on color based segmentation, SURF descriptor for feature extraction and MLP for recognition. For using SURF descriptor, Traffic signs are detected and classified after extraction based on fuzzy rules segmentation which provides robustness and also gives a very high accuracy for static detection and recognition.…”

Section: Review Based On Frameworkmentioning

confidence: 99%

“…Chakraborty and Deb in [18] [26]. Using syntactically generated datasets, most shapes including quadrangular can be detected in sunny conditions at an accuracy of 95% and recognized at a rate of 97% accuracy.…”

Section: Review Based On Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Nandi¹,

Saif²,

Paul³

et al. 2018

IJMECS

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Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.

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Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

“…The most widely used supervised learning approaches for traffic sign recognition are ANN Ref. [6,25,26,28,36,38,40],SVM Ref. [12,21,23], Random Forest Ref.…”

Section: Analysis Aspects Of Traffic Sign Detection and Recognitionmentioning

confidence: 99%

Section: Review Based On Frameworkmentioning

confidence: 99%

Section: Review Based On Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Nandi¹,

Saif²,

Paul³

et al. 2018

IJMECS

View full text Add to dashboard Cite

show abstract

“…In the traffic sign recognition research field, numerous methods and approaches are used. Segmentation by colour is often used to detect areas where traffic signs appear like RGB [3,4,8,15] or HSV/HSI colour model [5,11,12]. Other methods that track corners and edges were used in the shape analysis [9].…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Shape Detection and Classification based on the Segment Surface Occupancy Analysis and Correlation Comparisons

2018

Teh. vjesn.

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This article addresses the issue of traffic sign recognition. It contributes to a growing body of research done by the automotive industry due to a necessity for ensuring better safety on the roads. This paper presents a novel method for traffic signs recognition. The implementation of the whole process of traffic sign recognition has a step-wise nature but the novelty is introduced into the traffic sign shape detection stage. The method is based on a new approach for traffic sign shape recognition based on the image content occupancy analysis. Further, the traffic sign content classification is based on a simplistic relational correlation analysis. The tests were performed on image data comprising various roads and lighting conditions. The test includes different sizes of templates used in the correlation comparison method. The results are presented in a manner of successfulness of the correct recognition.

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Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition

Yang

Luo

Chung

et al. 2019

Lecture Notes in Networks and Systems

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Traffic sign detection and recognition using fuzzy segmentation approach and artificial neural network classifier respectively

Cited by 18 publications

References 13 publications

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Traffic Sign Detection based on Color Segmentation of Obscure Image Candidates: A Comprehensive Study

Traffic Sign Shape Detection and Classification based on the Segment Surface Occupancy Analysis and Correlation Comparisons

Simplified Neural Networks with Smart Detection for Road Traffic Sign Recognition

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