  To ensure flight safety of aircraft structures, it is necessary to have
regular maintenance using visual and nondestructive inspection (NDI) methods.
In this paper, we propose an automatic image-based aircraft defect detection
using Deep Neural Networks (DNNs). To the best of our knowledge, this is the
first work for aircraft defect detection using DNNs. We perform a comprehensive
evaluation of state-of-the-art feature descriptors and show that the best
performance is achieved by vgg-f DNN as feature extractor with a linear SVM
classifier. To reduce the processing time, we propose to apply SURF key point
detector to identify defect patch candidates. Our experiment results suggest
that we can achieve over 96% accuracy at around 15s processing time for a
high-resolution (20-megapixel) image on a laptop.
