Abstract: Traffic sign detection and classification is a crucial task in the field of autonomous driving, driver assistance systems, and traffic control. The objective is to propose a method that involves training a CNN on a large dataset of traffic sign images, which allows the network to learn the relevant features and patterns required for accurate detection and classification. Working on multiple datasets of standard benchmark and others helps to explore the difficulties and short comings of a CNN model proposed. Results are aimed to be helping in correct detection of a traffic sign and reducing the loss also using the GUI with the help of Tkinter.

Keywords:  Convolutional Neural Network (CNN), Graphical user interface (GUI), Dataset, Tkinter.

PDF | DOI: 10.17148/IJARCCE.2023.124149

Open chat
Chat with IJARCCE