Car Traffic Sign Recognizer Using Convolutional Neural Network CNN

Lodhi, Abhay; Singhal, Sagar; Massoudi, Massoud

doi:10.1109/icict50816.2021.9358594

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have attempted to increase the accuracy of traffic sign recognition using a variety of methods [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In general, there are three categories of traffic signs in China: indication, warning and prohibition, which are represented by blue, yellow and red, respectively.…”

Section: Related Workmentioning

confidence: 99%

Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Liu

Cai

Tang

et al. 2022

Entropy

View full text Add to dashboard Cite

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.

show abstract