Road Sign Detection and Recognition of Thai Traffic Based on YOLOv3

2019 ◽  
pp. 271-279
Author(s):  
Paitoon Thipsanthia ◽  
Rapeeporn Chamchong ◽  
Panida Songram
Keyword(s):  
Road Sign ◽  
Sign Detection ◽  
Detection And Recognition

Road-Sign Detection and Recognition Based on Support Vector Machines

2007 ◽  
Vol 8 (2) ◽  
pp. 264-278 ◽  
Author(s):  
Saturnino Maldonado-Bascon ◽  
Sergio Lafuente-Arroyo ◽  
Pedro Gil-Jimenez ◽  
Hilario Gomez-Moreno ◽  
Francisco Lopez-Ferreras
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Road Sign ◽  
Vector Machines ◽  
Sign Detection ◽  
Detection And Recognition

scholarly journals Speed Limit Road Sign Detection and Recognition System

2015 ◽  
Vol 131 (2) ◽  
pp. 43-50
Author(s):  
Fakhradeen Hamid ◽  
Mohammad Haqqi
Keyword(s):  
Recognition System ◽  
Speed Limit ◽  
Road Sign ◽  
Sign Detection ◽  
Detection And Recognition

scholarly journals Automatic road sign detection and recognition based on neural network

2021 ◽  
Author(s):  
Redouan Lahmyed ◽  
Mohamed El Ansari ◽  
Zakaria Kerkaou
Keyword(s):  
Clustering Algorithm ◽  
Color Space ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Traffic Sign ◽  
Initial Image ◽  
Dbscan Clustering ◽  
Road Sign ◽  
Sign Detection ◽  
Detection And Recognition

Abstract Road sign detection and recognition is an integral part of intelligent transportation sys-tems (ITS). It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using Artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns (GLPB) feature to the RGB color space and the local self-similarity (LSS) feature. ANN, Adaboost and support vector machine (SVM) have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databasets, well known in the traffic sign community (GTSRB, GTSDB and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods.

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