Citation

Suhaimi, Nur Syakira and Goh, Vik Tor and Yap, Timothy Tzen Vun and Ng, Hu (2024) Detecting and Extracting Illegal Signs from Video. International Journal of Integrated Engineering, 16 (3). pp. 100-106. ISSN 2229-838X

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Abstract

This project focuses on developing an automated system to detect illegal signs in urban environments from videos. The system utilizes computer vision and machine learning techniques, specifically the YOLOv5 object detection framework, to accurately identify and locate illegal signs in video frames. It incorporates a verification process usingOptical Character Recognition (OCR) to differentiate between legal and illegal signs based on the extracted text information. The system is designed as a user-friendly web application, allowing users to upload videos or images for analysis and receive comprehensive results. The system can achieve a detection accuracy of up to 78.6%. With this system, authorities can effectively manage and regulate illegal signs in urban areas, contributing to better urban landscapes.

Item Type:	Article
Uncontrolled Keywords:	Machine learning techniques, urban, computer vision
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 May 2024 07:22
Last Modified:	31 May 2024 07:22
URII:	http://shdl.mmu.edu.my/id/eprint/12511

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