چکیده
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This study proposes the exploitation of deep learning for quantitative assessment of visual detectability of different
types of in‐service damage in laminated composite structures such as aircraft and wind turbine blades. A
comprehensive image‐based data set is collected from the literature containing common microscale damage
mechanisms (matrix cracking and fibre breakage) and macroscale damage mechanisms (impact and erosion).
Then, automated classification of the damage type and severity was done by pre‐trained version of AlexNet that
is a stable convolutional neural network for image processing. Pre‐trained ResNet‐50 and 5 other user‐defined
convolutional neural networks were also used to evaluate the performance of AlexNet. The results demonstrated
that employing AlexNet network, using the relatively small image dataset, provided the highest accuracy
level (87%–96%) for identifying the damage severity and types in a reasonable computational time. The
generated knowledge and the collected image data in this paper will facilitate further research and development
in the field of autonomous visual inspection of composite structures with the potential to significantly
reduce the costs, health & safety risks and downtime associated with integrity assessment.
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