Open AccessFlying Metrology and Defect Identification for Aircraft Surface Inspection
Author(s) -
Buckhorst Armin F.,
KlugeWilkes Aline,
Schmitt Robert H.
Publication year - 2019
Publication title -
photonicsviews
Language(s) - English
Resource type - Journals
eISSN - 2626-1308
pISSN - 2626-1294
DOI - 10.1002/phvs.201900009
Subject(s) - visual inspection , identification (biology) , metrology , computer vision , artificial intelligence , machine vision , computer science , automated x ray inspection , lightning (connector) , engineering , real time computing , automotive engineering , image (mathematics) , image processing , power (physics) , statistics , physics , mathematics , quantum mechanics , biology , botany
A commercial aircraft is struck by lightning on average once a year. To rule out possible damage to the aircraft, a time‐consuming visual inspection of the aircraft is carried out by maintenance staff. In order to reduce overall maintenance costs and aircraft on ground time, an autonomous unpiloted aerial vehicle is utilized. The mobile unit allows an easy inspection of the compromised area, by carrying industrial camera technology to digitalize the aircrafts’ surface. So this approach applies and extends classic machine vision as well as machine learning algorithms to automatically detect maintenance‐relevant surface defects in location‐indexed images recorded by the system.