• Detecting Underwater Objects (DUO)
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Detecting Underwater Objects (DUO)

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.

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Citation

@ARTICLE{2021arXiv210605681L,
author = {{Liu}, Chongwei and {Li}, Haojie and {Wang}, Shuchang and {Zhu}, Ming and {Wang}, Dong and {Fan}, Xin and {Wang}, Zhihui},
title = "{A Dataset And Benchmark Of Underwater Object Detection For Robot Picking}",
journal = {arXiv e-prints},
year = 2021,
month = jun,
eid = {arXiv:2106.05681},
pages = {arXiv:2106.05681},
archivePrefix = {arXiv},
eprint = {2106.05681},
primaryClass = {cs.CV}
}