Proceedings ArticleDOI
Machine learning approaches for defect classification on aircraft fuselage images aquired by an UAV
Julien Miranda,Jannic Veith,Stanislas Larnier,Ariane Herbulot,Michel Devy +4 more
- Vol. 11172, pp 1117208
TLDR
A method to combine different approaches in order to achieve best performances on categorical accuracy, with special attention paid to underrepresented category is proposed.Abstract:
In order to ease visual inspections of exterior aircraft fuselage, new technical approaches have been recently deployed. Automated UAVs are now acquiring high quality images of the aircraft in order to perform offline analysis. At first, some acquisitions are annotated by human operators in order to provide a large dataset required to train machine learning methods, especially for critical defects detection. An intrinsic problem of this dataset is its extreme imbalance (i.e there is an unequal distribution between classes): the rarest and most valuable samples represent few elements among thousands of annotated objects. Deep Learning-only based approaches have proven to be very effective when a sufficient amount of data is available for each desired class, whereas less complex systems such as Support Vector Machine theoretically need less data, and few-shot learning dedicated methods (Matching Network, Prototypical Network, etc.) can learn from only few examples. Those approaches are compared on our applicative case. Preliminary results show the existence of empirical frontiers in term of training dataset volume that indicate which approach might be favored. Based on those results, we propose a method to combine different approaches in order to achieve best performances on categorical accuracy, with special attention paid to underrepresented category.read more
Citations
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Journal ArticleDOI
Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection
TL;DR: A hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance and is shown to increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure.
Journal ArticleDOI
Aircraft visual inspection: A systematic literature review
TL;DR: In this article , the authors present a systematic literature review of methods and techniques used in procedures for the visual inspection of aircraft and also show some insights into the automation of these processes with robotics and computer vision.
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Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure
TL;DR: In this article , the authors provide a brief overview of machine learning and deep learning techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc.
Journal ArticleDOI
Aircraft Fuselage Corrosion Detection Using Artificial Intelligence.
Bruno Brandoli,André R. de Geus,Jefferson R. Souza,Gabriel Spadon,Amilcar Soares,Jose F. Rodrigues,Jerzy Komorowski,Stan Matwin +7 more
TL;DR: In this article, the authors proposed a methodology for automatic image-based corrosion detection of aircraft structures using deep neural networks, which can support specialists and engineers in corrosion monitoring in the aerospace industry, potentially contributing to the automation of condition-based maintenance protocols.
Journal ArticleDOI
A UAV-Based Aircraft Surface Defect Inspection System via External Constraints and Deep Learning
TL;DR: A novel system based on the unmanned aerial vehicle (UAV) is presented to achieve automated aircraft surface inspection efficiently and demonstrates the superiority of the proposed external localization module and the effectiveness of the crack detection module.
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