Sithmini Gunasekara

Work place: Department of Electrical and Electronic Engineering, University of Peradeniya, Kandy, 20000, Sri Lanka



Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes, Computational Learning Theory, Engineering


Sithmini Gunasekara received her bachelor’s degree with Second Class Honors (Upper Division) in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka in 2021. In the same year she joined the Department of Electrical and Electrical Engineering, University of Peradeniya as an instructor. Her research interests are machine learning and communication engineering.

Author Articles
Deep Learning Based Autonomous Real-Time Traffic Sign Recognition System for Advanced Driver Assistance

By Sithmini Gunasekara Dilshan Gunarathna Maheshi B. Dissanayake Supavadee Aramith Wazir Muhammad

DOI:, Pub. Date: 8 Dec. 2022

Deep learning (DL) architectures are becoming increasingly popular in modern traffic systems and self-driven vehicles owing to their high efficiency and accuracy. Emerging technological advancements and the availability of large databases have made a favorable impact on such improvements. In this study, we present a traffic sign recognition system based on novel DL architectures, trained and tested on a locally collected traffic sign database. Our approach includes two stages; traffic sign identification from live video feed, and classification of each sign. The sign identification model was implemented with YOLO architecture and the classification model was implemented with Xception architecture. The input video feed for these models were collected using dashboard camera recordings. The classification model has been trained with the German Traffic Sign Recognition Benchmark dataset as well for comparison. Final accuracy of classification for the local dataset was 96.05% while the standard dataset has given an accuracy of 92.11%. The final model is a combination of the detection and classification algorithms and it is able to successfully detect and classify traffic signs from an input video feed within an average detection time of 4.5fps

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