Journal of Aeronautical Engineering

Journal of Aeronautical Engineering

Detection of foreign object debris (active and inactive) and cracks on the runway using machine vision techniques

Document Type : Original Article

Authors
Mehdi Sabzehparvar 1
mostafa abbasi azandaryani 2
1 Aerospace Engineering Amirkabir Univ. of Technology Tehran,Iran
2 Amirkabir University of Technology
10.22034/joae.2022.303523.1045
Abstract
The purpose of this article is to use machine vision techniques, and to process images by the camera to identify and detect the remains of foreign objects on the runway. Recognizing the remains of foreign objects on the runway of airports is a significant issue that can cause serious damage to the aircraft and delays in the air transport system and many financial and human losses. The proposed solution is based on machine vision system and video processing. The main purpose of the proposed algorithm is to detect the remains of foreign objects, and a large number of unknown objects that can’t be categorized. The safest way to diagnose based on machine vision in the runway of airports is to monitor the area on a frame-by-frame basis and use background subtraction algorithms. Using near-realistic data in the form of video and their application to the detection algorithm. The algorithm is able to detect the remains of active and inactive foreign objects with a very small size of 0.0023 (square meters) and 79% efficiency, parts in medium dimensions with an area of 0.2201 (square meters) And 98% efficiency, detects fire, bird movements and cracks with high accuracy and the necessary warnings (high and low level) (audible (siren) and display on the air traffic controller. Fully graphical and user-friendly display, provide the exact dimensions, size and location of the foreign objects (active and inactive) to the airport inspection and firefighting teams.
Keywords
Camera
Machine vision
Foreign object debris
Cracks
Airport Runway

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Journal of Aeronautical Engineering
Volume 24, Issue 1
April 2022
Pages 16-26

  • Receive Date 07 September 2021
  • Revise Date 20 December 2021
  • Accept Date 10 May 2022