Road-Sign Detection and Recognition Based on Support Vector Machines

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Download Full-text

Speed limit traffic sign detection and recognition based on support vector machines

2017 International Conference on Advanced Technologies for Communications (ATC) ◽

10.1109/atc.2017.8167633 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hung Ngoc Do ◽

Minh-Thanh Vo ◽

Huy Quoc Luong ◽

An Hoang Nguyen ◽

Kien Trang ◽

...

Keyword(s):

Support Vector Machines ◽

Support Vector ◽

Speed Limit ◽

Traffic Sign ◽

Vector Machines ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

Download Full-text

Traffic Sign Detection and Pattern Recognition Based on Binary Tree Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.204-210.1394 ◽

2011 ◽

Vol 204-210 ◽

pp. 1394-1398

Author(s):

Fa Ming Gong ◽

Hai Juan Li

Keyword(s):

Binary Tree ◽

Feature Vector ◽

Recognition System ◽

Support Vector ◽

Shape Classification ◽

Traffic Sign ◽

Road Sign ◽

Vector Machines ◽

Sign Detection ◽

Traffic Sign Detection

This paper presents an automatic road sign detection and recognition system based on binary tree SVM. Color based segmentation techniques are employed for traffic sign detection. The coordinates position of traffic sign in images used for shape classification are obtained by orthogonal projection. An algorithm based on Hough transform was proposed to achieve better shape classification performance.Recognition of traffic signs are implemented using binary tree multi- classifer SVM with geometry semantic feature as the feature vector

Download Full-text

Automatic road sign detection and recognition based on neural network

10.21203/rs.3.rs-408446/v1 ◽

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.

Download Full-text