Detecting and Extracting Illegal Signs from Video

Nur Syakira Suhaimi, Vik Tor Goh*, Timothy Tzen Vun Yap, Hu Ng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 using Optical 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.

Original languageEnglish
Pages (from-to)100-106
Number of pages7
JournalInternational Journal of Integrated Engineering
Volume16
Issue number3
DOIs
Publication statusPublished - 29 Apr 2024

Keywords

  • frame extraction
  • illegal signs
  • optical character recognition
  • YOLO

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Materials Science (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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