  Detection of artificial objects from underwater imagery gathered by
Autonomous Underwater Vehicles (AUVs) is a key requirement for many subsea
applications. Real-world AUV image datasets tend to be very large and
unlabelled. Furthermore, such datasets are typically imbalanced, containing few
instances of objects of interest, particularly when searching for unusual
objects in a scene. It is therefore, difficult to fit models capable of
reliably detecting these objects. Given these factors, we propose to treat
artificial objects as anomalies and detect them through a semi-supervised
framework based on Variational Autoencoders (VAEs). We develop a method which
clusters image data in a learned low-dimensional latent space and extracts
images that are likely to contain anomalous features. We also devise an anomaly
score based on extracting poorly reconstructed regions of an image. We
demonstrate that by applying both methods on large image datasets, human
operators can be shown candidate anomalous samples with a low false positive
rate to identify objects of interest. We apply our approach to real seafloor
imagery gathered by an AUV and evaluate its sensitivity to the dimensionality
of the latent representation used by the VAE. We evaluate the precision-recall
tradeoff and demonstrate that by choosing an appropriate latent dimensionality
and threshold, we are able to achieve an average precision of 0.64 on
unlabelled datasets.
