  One use of Autonomous Underwater Vehicles (AUVs) is the monitoring of
habitats associated with threatened, endangered and protected marine species,
such as the handfish of Tasmania, Australia. Seafloor imagery collected by AUVs
can be used to identify individuals within their broader habitat context, but
the sheer volume of imagery collected can overwhelm efforts to locate rare or
cryptic individuals. Machine learning models can be used to identify the
presence of a particular species in images using a trained object detector, but
the lack of training examples reduces detection performance, particularly for
rare species that may only have a small number of examples in the wild. In this
paper, inspired by recent work in few-shot learning, images and annotations of
common marine species are exploited to enhance the ability of the detector to
identify rare and cryptic species. Annotated images of six common marine
species are used in two ways. Firstly, the common species are used in a
pre-training step to allow the backbone to create rich features for marine
species. Secondly, a copy-paste operation is used with the common species
images to augment the training data. While annotations for more common marine
species are available in public datasets, they are often in point format, which
is unsuitable for training an object detector. A popular semantic segmentation
model efficiently generates bounding box annotations for training from the
available point annotations. Our proposed framework is applied to AUV images of
handfish, increasing average precision by up to 48\% compared to baseline
object detection training. This approach can be applied to other objects with
low numbers of annotations and promises to increase the ability to actively
monitor threatened, endangered and protected species.
