A novel aircraft wing inspection framework based on multiple view geometry and convolutional neural network

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.