Jerzy Komorowski
Jerzy Komorowski has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Amilcar Soares
Linnaeus University, Växjö, Sweden
1 shared publication - André R. de Geus
Department of Computer Science, Federal University of Uberlandia, Uberlandia 38400-902, Brazil
1 shared publication - Bruno Brandoli
Institute for Big Data Analytics, Dalhousie University, Halifax, Canada
1 shared publication - Gabriel Spadon
Dalhousie University, Halifax, Canada
1 shared publication - Jefferson R. Souza
Federal University of Uberlândia
1 shared publication - Jose Fernando Rodrigues Junior
University of São Paulo, São Carlos, Brazil
1 shared publication
Latest Publications
- Open Access
Aircraft Fuselage Corrosion Detection Using Artificial Intelligence
Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.
Open AccessAircraft Fuselage Corrosion Detection Using Artificial Intelligence
Corrosion identification and repair is a vital task in aircraft maintenance to ensure continued structural integrity. Regarding fuselage lap joints, typically, visual inspections are followed by non-destructive methodologies, which are time-consuming. The visual inspection of large areas suffers not only from subjectivity but also from the variable probability of corrosion detection, which is aggravated by the multiple layers used in fuselage construction. In this paper, we propose a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks. For machine learning, we use a dataset that consists of D-Sight Aircraft Inspection System (DAIS) images from different lap joints of Boeing and Airbus aircrafts. We also employ transfer learning to overcome the shortage of aircraft corrosion images. With precision of over 93%, we demonstrate that our approach detects corrosion with a precision comparable to that of trained operators, aiding to reduce the uncertainties related to operator fatigue or inadequate training. Our results indicate that our methodology can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.