  Human enterprise often suffers from direct negative effects caused by
jellyfish blooms. The investigation of a prior jellyfish monitoring system
showed that it was unable to reliably perform in a cross validation setting,
i.e. in new underwater environments. In this paper, a number of enhancements
are proposed to the part of the system that is responsible for object
classification. First, the training set is augmented by adding synthetic data,
making the deep learning classifier able to generalise better. Then, the
framework is enhanced by employing a new second stage model, which analyzes the
outputs of the first network to make the final prediction. Finally, weighted
loss and confidence threshold are added to balance out true and false
positives. With all the upgrades in place, the system can correctly classify
30.16% (comparing to the initial 11.52%) of all spotted jellyfish, keep the
amount of false positives as low as 0.91% (comparing to the initial 2.26%) and
operate in real-time within the computational constraints of an autonomous
embedded platform.
