Citation:
Kuang B, Rana Z, Zhao Y. (2020) A novel aircraft wing inspection framework based on multiple view geometry and convolutional neural network. In: Aerospace Europe Conference 2020 (AEC2020), Bordeaux, France, 25-28 February 2020
Abstract:
To achieve greener and safer aeronautical
operations, this paper considers the problem of
reconstructing the three-dimensional (3D)
geometric structure of aeronautical components. A
novel framework that recovers the 3D shapes by
means of convolutional neural network (ConvNets)
and multiple view geometry (MVG) operating on
Mask-R-CNN-segmented two-dimensional images
is proposed. To achieve more accurate 3D
aircraft’s surface and exclude the invalid
background structures, this paper innovatively
integrates the environmental robustness of
ConvNets and geometric adaptation of Mask-R-
CNN into the MVG theory. The preliminary
experiments show that the proposed framework is
visual-comfortable, and it also accurately
reconstructs the regions with damage to catch up
with the inspection purpose.