Home
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Log In
    New user? Click here to register. Have you forgotten your password?
Home
  • Browse Our Collections
  • Publications
  • Researchers
  • Research Data
  • Institutions
  • Statistics
    • English
    • Čeština
    • Deutsch
    • Español
    • Français
    • Gàidhlig
    • Latviešu
    • Magyar
    • Nederlands
    • Português
    • Português do Brasil
    • Suomi
    • Log In
      New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Resources
  3. UniMAP Index Publications
  4. Publications 2023
  5. Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application
 
Options

Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application

Journal
Journal of Physics: Conference Series
ISSN
17426588
Date Issued
2023-01-01
Author(s)
Jack Lee L.
Hazry Desa
Universiti Malaysia Perlis
Muhammad Azizi A.
Abadal-Salam T.H.
Hassan T.M.
DOI
10.1088/1742-6596/2641/1/012009
Abstract
This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm's accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm's reliable performance in predicting distance measurements.
File(s)
Research repository notification.pdf (4.4 MB)
google-scholar
Views
Downloads
  • About Us
  • Contact Us
  • Policies