@Article{cmc.2023.035595,
AUTHOR = {Sara Khalid, Jamal Hussain Shah, Muhammad Sharif, Muhammad Rafiq, Gyu Sang Choi},
TITLE = {Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {1},
PAGES = {861--879},
URL = {http://www.techscience.com/cmc/v76n1/53045},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.035595}
}