Underwater object detection for robot picking has attracted a lot of
interest. However, it is still an unsolved problem due to several challenges.
We take steps towards making it more realistic by addressing the following
challenges. Firstly, the currently available datasets basically lack the test
set annotations, causing researchers must compare their method with other SOTAs
on a self-divided test set (from the training set). Training other methods lead
to an increase in workload and different researchers divide different datasets,
resulting there is no unified benchmark to compare the performance of different
algorithms. Secondly, these datasets also have other shortcomings, e.g., too
many similar images or incomplete labels. Towards these challenges we introduce
a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark,
based on the collection and re-annotation of all relevant datasets. DUO
contains a collection of diverse underwater images with more rational
annotations. The corresponding benchmark provides indicators of both efficiency
and accuracy of SOTAs (under the MMDtection framework) for academic research
and industrial applications, where JETSON AGX XAVIER is used to assess detector
speed to simulate the robot-embedded environment