  Visual defect assessment is a form of anomaly detection. This is very
relevant in finding faults such as cracks and markings in various surface
inspection tasks like pavement and automotive parts. The task involves
detection of deviation/divergence of anomalous samples from the normal ones.
Two of the major challenges in supervised anomaly detection are the lack of
labelled training data and the low availability of anomaly instances.
Semi-supervised methods which learn the underlying distribution of the normal
samples and then measure the deviation/divergence from the estimated model as
the anomaly score have limitations in their overall ability to detect
anomalies. This paper proposes the application of network-based deep transfer
learning using convolutional neural networks (CNNs) for the task of anomaly
detection. Single class SVMs have been used in the past with some success,
however we hypothesize that deeper networks for single class classification
should perform better. Results obtained on established anomaly detection
benchmarks as well as on a real-world dataset, show that the proposed method
clearly outperforms the existing state-of-the-art methods, by achieving a
staggering average area under the receiver operating characteristic curve value
of 0.99 for the tested data-sets which is an average improvement of 41% on the
CIFAR10, 20% on MNIST and 16% on Cement Crack data-sets.
