Volume & Issue no: Volume 11, Issue 5, September - October 2022
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Title: |
Colour Based Traffic Sign Detection and Recognition using Support Vector Machine (SVM), Convolutional Neural Networks (CNN) |
Author Name: |
Rajesh V, Dr. Rajashekar J S |
Abstract: |
Todays world uses artificial intelligence to
automate nearly every field, simplifying everything. This
decreases risk while simultaneously increasing accuracy,
dependability, and minimizing physical contact. While
operating a car, it is crucial to adhere to traffic laws and
regulations, but occasionally drivers fail to see the signs
that are posted along the sides of the road. This can be fatal
for both drivers and pedestrians. Hence the system has to be
integrated with ADAS system, which will automatically
send the driver a text or voice message to warn them of an
approaching traffic sign. In this paper we proposed the
model which will detect the traffic sign in diverse
background using color information and SVM. Detected
Traffic sign is recognized and classified using
convolutional neural network. We used Lenet-5 CNN
architecture and was found more efficient about traditional
CNN model. Thus, making it desirable to apply in real-time
computer vision tasks. Proposed design has high detection
rate and it is having less complexity |
Cite this article: |
Rajesh V, Dr. Rajashekar J S , "
Colour Based Traffic Sign Detection and Recognition using Support Vector Machine (SVM), Convolutional Neural Networks (CNN)" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 11, Issue 5, September - October 2022 , pp.
013-016 , ISSN 2278-6856.
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