@Article{iasc.2023.036981,
AUTHOR = {Kha Tu Huynh, Thi Phuong Linh Le, Muhammad Arif, Thien Khai Tran},
TITLE = {A Deep Learning Model of Traffic Signs in Panoramic Images Detection},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {1},
PAGES = {401--418},
URL = {http://www.techscience.com/iasc/v37n1/52638},
ISSN = {2326-005X},
ABSTRACT = {To pursue the ideal of a safe high-tech society in a time when
traffic accidents are frequent, the traffic signs detection system has become
one of the necessary topics in recent years and in the future. The ultimate
goal of this research is to identify and classify the types of traffic signs in a
panoramic image. To accomplish this goal, the paper proposes a new model
for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional
Neural Networks (R-CNN) implementation for identifying and extracting
signs in panoramic images. Data augmentation and normalization of the
images are also applied to assist in classifying better even if old traffic signs are
degraded, and considerably minimize the rates of discovering the extra boxes.
The proposed model is tested on both the testing dataset and the actual images
and gets 94.5% of the correct signs recognition rate, the classification rate of
those signs discovered was 99.41% and the rate of false signs was only around
0.11.},
DOI = {10.32604/iasc.2023.036981}
}