A Deep Convolutional Neural Network for Traffic Sign Classification and Detection with Voice Recognition
Main Article Content
Abstract
Traffic Sign Detection and Classification play an important major role nowadays in our daily life. Anyway, Traffic Signs are present on roads. Even though, often the drivers do mistakes. It’s very hard to recognize and detect traffic signs while travelling on roads. Drivers may misinterpret traffic signs, this leads to Accidents and results in damage to the vehicle. To overcome this problem this project introduces a concept named Traffic Sign Detection and Classification with Voice Recognition. This model is built by using CNN to extract the images and classify the traffic signs. Here DCCNN model is built to improve overall accuracy and speed. Here Classification process is also tested with AlexNet, VGG16, VGG19. This system reveals output that recognizes the traffic signs automatically that helps to detect the street condition and alerts driver soon and this enables to build a smart vehicle.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
