Paper Title :Deep Learning-Based Smart Traffic Signage to Control Highway Speed Limit
Author :Ahmed Yunis, Ammar Aldallal, Fatima Nasaif
Article Citation :Ahmed Yunis ,Ammar Aldallal ,Fatima Nasaif ,
(2023 ) " Deep Learning-Based Smart Traffic Signage to Control Highway Speed Limit " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 13-17,
Volume-11,Issue-11
Abstract : This Speeding is a major contributing factor to road accidents and fatalities worldwide. To improve road safety,
speed signs are commonly utilized to communicate speed limits and provide guidance to drivers. This project aims to
enhance an existing system that utilizes cameras and machine learning to determine road conditions and the appropriate
speed limit specifically during rainy weather by developing smart speed limit traffic sign (SSLTS). The proposed system
involves optimizing the image processing through enhanced convolutional neural network algorithm to properly detect
raindrops. An Arduino microcontroller device reads the output of the raindrop detection system. Based on the prevailing
weather conditions, the system displays the corresponding speed limit on an LCD screen. This project leverages advanced
technology to bolster public safety by promptly notifying drivers of the appropriate speed limit during rainy conditions,
potentially reducing the risk of accidents.
Keywords - Smart Traffic Sign, Convolutional Neural Network (Cnn), Speed Limit, Machine Learning
Type : Research paper
Published : Volume-11,Issue-11
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-20321
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Copyright: © Institute of Research and Journals
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Published on 2024-01-23 |
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