voice-assisted traffic sign board detection using convolutional neural network
Sowmiya ,Sneha B A,Abirami V,Suresh S
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 26 April,2023 Pages:1815-1820
Abstract
Traffic sign detection has great achievable for smart vehicles. In current years, traffic sign detection has made huge progress with the growth of deep learning. A primary reason of road accidents is negligence in viewing the Traffic signboards and interpreting them incorrectly. The proposed system is trained using a Convolutional Neural Network (CNN), which helps in the detection and classification of traffic signs. The proposed system helps in recognizing the Traffic sign and produce a voice assist to the driver so that he/ she might also take necessary decisions. To enhance the accuracy, a set of classes is described and trained on a particular dataset. The German Traffic Sign Benchmarks Dataset (GTSRB), which includes approximately 43 classes and 50,000 traffic sign images, was used. The accuracy of the execution is about 0.969. The proposed system additionally includes a section where the vehicle driver is alerted about the traffic signs in the close to proximity which helps them to be aware of what policies to follow on the route. The aim of this system is to make sure the protection of the vehicle's driver, passengers, and pedestrians.
Kewords
Convolutional Neural Network (CNN), German Traffic Sign Benchmarks Dataset (GTSRB)
Reference
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