Open Access
ARTICLE
Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features
Sara Khalid1, Jamal Hussain Shah1,*, Muhammad Sharif1, Muhammad Rafiq2, Gyu Sang Choi3,*
1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, 47040, Pakistan
2 Department of Game and Mobile Engineering, Keimyung University, Daegu, 42601, Korea
3 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
* Corresponding Authors: Jamal Hussain Shah. Email: ; Gyu Sang Choi. Email:
(This article belongs to this Special Issue: Recent Advances in Hyper Parameters Optimization, Features Optimization, and Deep Learning for Video Surveillance and Biometric Applications)
Computers, Materials & Continua 2023, 76(1), 861-879. https://doi.org/10.32604/cmc.2023.035595
Received 27 August 2022; Accepted 09 February 2023; Issue published 08 June 2023
Abstract
Globally traffic signs are used by all countries for healthier traffic
flow and to protect drivers and pedestrians. Consequently, traffic signs have
been of great importance for every civilized country, which makes researchers
give more focus on the automatic detection of traffic signs. Detecting these
traffic signs is challenging due to being in the dark, far away, partially
occluded, and affected by the lighting or the presence of similar objects. An
innovative traffic sign detection method for red and blue signs in color images
is proposed to resolve these issues. This technique aimed to devise an efficient,
robust and accurate approach. To attain this, initially, the approach presented
a new formula, inspired by existing work, to enhance the image using red and
green channels instead of blue, which segmented using a threshold calculated
from the correlational property of the image. Next, a new set of features is
proposed, motivated by existing features. Texture and color features are fused
after getting extracted on the channel of Red, Green, and Blue (RGB), Hue,
Saturation, and Value (HSV), and YCbCr color models of images. Later, the
set of features is employed on different classification frameworks, from which
quadratic support vector machine (SVM) outnumbered the others with an
accuracy of 98.5%. The proposed method is tested on German Traffic Sign
Detection Benchmark (GTSDB) images. The results are satisfactory when
compared to the preceding work.
Keywords
Cite This Article
S. Khalid, J. H. Shah, M. Sharif, M. Rafiq and G. S. Choi, "Traffic sign detection with low complexity for intelligent vehicles based on hybrid features,"
Computers, Materials & Continua, vol. 76, no.1, pp. 861–879, 2023.