  Anomaly detection is important for industrial automation and part quality
assurance, and while humans can easily detect anomalies in components given a
few examples, designing a generic automated system that can perform at human or
above human capabilities remains a challenge. In this work, we present a simple
new anomaly detection algorithm called FADS (feature-based anomaly detection
system) which leverages pretrained convolutional neural networks (CNN) to
generate a statistical model of nominal inputs by observing the activation of
the convolutional filters. During inference the system compares the
convolutional filter activation of the new input to the statistical model and
flags activations that are outside the expected range of values and therefore
likely an anomaly. By using a pretrained network, FADS demonstrates excellent
performance similar to or better than other machine learning approaches to
anomaly detection while at the same time FADS requires no tuning of the CNN
weights. We demonstrate FADS ability by detecting process parameter changes on
a custom dataset of additively manufactured lattices. The FADS localization
algorithm shows that textural differences that are visible on the surface can
be used to detect process parameter changes. In addition, we test FADS on
benchmark datasets, such as the MVTec Anomaly Detection dataset, and report
good results.
