  This work reviews the problem of object detection in underwater environments.
We analyse and quantify the shortcomings of conventional state-of-the-art
(SOTA) algorithms in the computer vision community when applied to this
challenging environment, as well as providing insights and general guidelines
for future research efforts. First, we assessed if pretraining with the
conventional ImageNet is beneficial when the object detector needs to be
applied to environments that may be characterised by a different feature
distribution. We then investigate whether two-stage detectors yields to better
performance with respect to single-stage detectors, in terms of accuracy,
intersection of union (IoU), floating operation per second (FLOPS), and
inference time. Finally, we assessed the generalisation capability of each
model to a lower quality dataset to simulate performance on a real scenario, in
which harsher conditions ought to be expected. Our experimental results provide
evidence that underwater object detection requires searching for "ad-hoc"
architectures than merely training SOTA architectures on new data, and that
pretraining is not beneficial.
