Next Article in Journal
Temperature Drift Compensation for Hemispherical Resonator Gyro Based on Natural Frequency
Previous Article in Journal
Integral T-Shaped Phantom-Dosimeter System to Measure Transverse and Longitudinal Dose Distributions Simultaneously for Stereotactic Radiosurgery Dosimetry
Article Menu

Export Article

Open AccessArticle
Sensors 2012, 12(5), 6415-6433;

Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models

Department of Engineering Science, National Cheng Kung University Taiwan, No.1, University Road, Tainan City 701, Taiwan
Author to whom correspondence should be addressed.
Received: 23 March 2012 / Revised: 8 May 2012 / Accepted: 9 May 2012 / Published: 15 May 2012
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [854 KB, uploaded 21 June 2014]


A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle’s speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance. View Full-Text
Keywords: road sign recognition; fuzzy inference; Adaboost classifier; support vector machine road sign recognition; fuzzy inference; Adaboost classifier; support vector machine
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Lin, C.-C.; Wang, M.-S. Road Sign Recognition with Fuzzy Adaptive Pre-Processing Models. Sensors 2012, 12, 6415-6433.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top