Paper Title
Traffic Sign Detection and Recognition using Deep Learning

Abstract
Abstract – This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, Traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremely regions on Gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labelled from street views. The post processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system. The proposed system is exhaustively apportioned into, data planning, data gathering, and getting ready and testing. System uses variety of picture planning strategies to improve the image quality and to oust non-illuminating pixel, and recognizing edges. Feature extractors are used to find the features of picture. Moved AI figuring Convolutional Neural Networks (CNN) is used to gather the differing traffic sign pictures reliant on their features by using the progressing camera. Keywords - Convolutional Neural Networks (CNN), Traffic sign regions of interest (ROIs)

Author - Vaishnav Patil, Vaishnav Shinde, Aniket Shelar, Kiran Shelke, Richa Agarwal

Published : Volume-9,Issue-12  ( Dec, 2022 )

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