In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at
Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN
Abstract—Current state-of-the-art object tracking methods have
largely benefited from the public availability of numerous benchmark
datasets. However, the focus has been on open-air imagery
and much less on underwater visual data. Inherent underwater
distortions, such as color loss, poor contrast, and underexposure,
caused by attenuation of light, refraction, and scattering, greatly
affect the visual quality of underwater data, and as such, existing
open-air trackers perform less efficiently on such data. To help
bridge this gap, this article proposes a first comprehensive underwater
object tracking (UOT100) benchmark dataset to facilitate
the development of tracking algorithms well-suited for underwater
environments. The proposed dataset consists of 104 underwater
video sequences and more than 74 000 annotated frames derived
from both natural and artificial underwater videos, with great
varieties of distortions. We benchmark the performance of 20
state-of-the-art object tracking algorithms and further introduce
a cascaded residual network for underwater image enhancement
model to improve tracking accuracy and success rate of trackers.
Our experimental results demonstrate the shortcomings of existing
tracking algorithms on underwater data and how our generative
adversarial network (GAN)-based enhancement model can
be used to improve tracking performance. We also evaluate the
visual quality of our model’s output against existing GAN-based
methods using well-accepted quality metrics and demonstrate that
our model yields better visual data.
Index Terms—Underwater benchmark dataset, underwater
generative adversarial network (GAN), underwater image
enhancement (UIE), underwater object tracking (UOT).
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- Award ID(s):
- 1942053
- NSF-PAR ID:
- 10309916
- Date Published:
- Journal Name:
- IEEE Journal of Oceanic Engineering
- ISSN:
- 0364-9059
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract http://warp.whoi.edu/vmat/ , (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild. -
Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JOE.2021.3086907
