  Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on
the Great Barrier Reef (GBR) and substantial surveillance and control programs
are ongoing to manage COTS populations to ecologically sustainable levels. In
this paper, we present a comprehensive real-time machine learning-based
underwater data collection and curation system on edge devices for COTS
monitoring. In particular, we leverage the power of deep learning-based object
detection techniques, and propose a resource-efficient COTS detector that
performs detection inferences on the edge device to assist marine experts with
COTS identification during the data collection phase. The preliminary results
show that several strategies for improving computational efficiency (e.g.,
batch-wise processing, frame skipping, model input size) can be combined to run
the proposed detection model on edge hardware with low resource consumption and
low information loss.
