Wazir Muhammad

Work place: Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar, 207124, Pakistan

E-mail: wazir.laghari@gmail.com

Website: https://orcid.org/0000-0002-3860-2213

Research Interests: Engineering, Image Processing, Computer Networks, Image Manipulation, Image Compression, Neural Networks, Computational Learning Theory, Computer systems and computational processes, Computational Engineering, Computational Science and Engineering, Electrical Engineering


Wazir Muhammad is a lecturer in the Electrical Engineering Department, Balochistan University of Engineering and Technology Khuzdar. He received his doctoral degree from the Department of Electrical Engineering, Chulalongkorn University, Bangkok, Thailand in 2019. He obtained his ME degree in the field of Communication Systems and Networks from Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan. His research interests lie in the areas of Electrical Engineering, Communication Systems, Neural Networks, and Machine Learning, specifically in Deep Learning Image Super-Resolution.

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: https://doi.org/10.5815/ijigsp.2022.06.06, 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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