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<h1>Computer Vision based Seafloor Surveying Technique</h1>
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<p><a href="http://saikit.org/members.html">The Vision, Graphics and
Computational Design (VGD) Group</a> at <a href="https://hkust.edu.hk/">HKUST</a> led by <a href="https://cse.hkust.edu.hk/admin/people/faculty/profile/saikit">Prof. Sai-Kit Yeung</a>
performs cutting-edge research on underwater object detection, semantic segmentation, simultaneous localization and mapping. The advanced computer vision techniques have been introduced for generating biology report, replacing with the traditional scuba diving and trawling based surveying.</p>
<p>The VGD group is now working with utilizing the underwater drones and object recognition for marine conservation. The group is to provide marine biologists, field ecologists, and coral conservationists with novel computer vision and 3D techniques to solve a wide range of marine related problems.</p>
<p>
Seafloors covered by marine soft sediments, which compose more than 70% of earth's seafloor, are also inhabited by a remarkable number of animals. Unfortunately, in recent decades, marine seafloor habitat is threatened by marine pollution. Hence, seafloor survey is an essential part in estimating biodiversity and evaluating the condition of the local marine ecosystem. Conventional methods for seafloor survey mainly rely on scuba diving and trawling. However, survey by scuba diving is costly, labor-intensive, low throughput, and highly limited by water depth and turbidity while seabed trawling is highly destructive and will result in major damage to the benthic environment.
</p>
<!-- <p>As it is well known, the underwater environment is low-light and turbid which significantly impairs the quality and visibility of underwater images. Namely, it is a challenging task to accurately and stably identify targets. To address this issue, we propose to introduce the generative adversarial network (GAN) for performing effective underwater image enhancement before conducting underwater object recognition and underwater visual SLAM (Simultaneous Localization and Mapping).</p> -->
<p>
We propose a new seafloor surveying technique by using latest underwater drone and simultaneous localization, mapping, and object recognition technology.
The key advantages over traditional method such as scuba diving and trawling is that we can perform high-throughput surveys while remaining non-invasive. The project will be divided into five main parts: 1) Data collection using underwater drone. 2) Marine object detection and classification algorithm development. 3) Underwater localization algorithm (SLAM) development. 4) System fusion, verification and optimization. 5) Final report on the seafloor condition.
</p>
<p>Our underwater drone:</p>
<!-- <div class="mb-2">
<iframe style="border:1px solid #999" width="100%" height="320" src="https://www.youtube.com/embed/j0-4f2KN0zk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
<br>
<p>
In our system, it is essential for robots to be able to be aware of their location. We aim to develop a robust visual SLAM system with the GAN-based underwater image enhancement, enhancing the capability of our underwater drone to localize itself. The underwater images' poor visibility, low contrast, and color distortion result in ineffective and insufficient feature matching, leading to the poor or even failure of the existing SLAM algorithms. The underwater enhancement could improvement the feature matching performance.
</p>
</div> -->
<div class="mb-2">
<a href="">
<img src="./MCEF _ HKUST CSE_files/rov.jpg" class="img-responsive" style="width: 100%; border:1px solid #999" alt="Our underwater drone.">
</a>
<p class="caption" style="text-align:left">
Our customized underwater drones equipped with multiple cameras for wide-angle video capture will be deployed at designated locations. The drones will be remotely controlled to move along a transect a fixed distance above the seafloor and capture video of the seafloor along the way.
</p>
</div>
<!-- <div class="mb-2">
<a href="./MCEF _ HKUST CSE_files/picture2.png">
<img src="./MCEF _ HKUST CSE_files/picture2.png" class="img-responsive" style="border:1px solid #999" alt="Dynamic light affects feature matching. To achieve real-time performance, feature-based SLAM utilizes ORB feature, an efficient but not illumination invariant feature. As the above pair of images show, although vast features are extracted, there are less matched features.">
</a>
<p class="caption" style="text-align:left">
Blue lines indicate correct feature matching while red lines present wrong feature matchings.
</p>
</div>
<p>
The real-time underwater image enhancement acts as the image pre-processing to build a robust and accurate underwater monocular SLAM system. With the introduction of real-time underwater image enhancement, we can significantly promote underwater SLAM performance.
</p>
<div class="mb-4">
<iframe style="border:1px solid #999" width="100%" height="475" src="https://www.youtube.com/embed/uJMFz9gjrBo" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div> -->
<p>
One of our goal is to perform underwater surveying, including detecting the underwater organism with regular shapes (e.g., sea urchin, sea cucumber, starfish and so on) and segmenting the corals with irregular boundaries.
</p>
<p>
Underwater object detection video demo:
</p>
<div class="mb-4">
<iframe style="border:1px solid #999" width="100%" height="460" src="https://www.youtube.com/embed/EkwFzEhPO-Q" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
</div>
<p>
In our system, it is essential for robots to be able to be aware of their location. We are going to further improve our SLAM system with multi-sensors fusion to enhance system’s robustness and increase localization accuracy. We will combine the marine species classification algorithm and the underwater localization algorithm. After integration of algorithms, the system will be able to not only identify marine specifies, but also localize them. The proposed system can facilitate robust data analysis and automatic underwater surveying.
</p>
<p>Semantic SLAM video demo:</p>
<div class="mb-4">
<iframe style="border:1px solid #999" width="100%" height="460" src="https://www.youtube.com/embed/zKacAH2Ozqk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
<p>In this video, we present the original image input, visualization with detected bounding boxes, visualization with 3D cube at the left top, middle and bottom.</p>
</div>
<p>
We hope our system can inspire the underwater research community to further develop and increase awareness of the general public on marine conservation.
</p>
<!-- <p>3D maps with coral labeled demo</p>
<img src="./MCEF _ HKUST CSE_files/coral.png" class="img-responsive" style="border:1px solid #999" alt=""> -->
<br>
<p><a href="http://saikit.org/opportunities.html">We are hiring!</a> If you are an ocean lover and want to develop new technology to solve different marine related problems, please email <script>mmsend('saikit','ust.hk')</script><a href="javascript:msend('saikit','ust.hk');">saikit@ust.hk</a> to join our research program.</p>
<!-- <p>[1] Huajian Huang, Daniel Lin, Siying Liu, Dong Zhang, Sai-Kit Yeung.
<a href="https://huajianup.github.io/research/Dual_SLAM/">Dual-SLAM: A framework for robust single camera navigation</a>.
<i>IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)</i>
</p> -->
<p>
[1] Ziqiang Zheng, Huajian Huang, Apple, Pui-Yi Chui, Yue Him Wong, Sai-Kit Yeung. Advanced Computer Vision Techniques for Efficient Seafloor Surveying.
<i>
International Conference on Biodiversity, Ecology and Conservation of Marine Ecosystems (BECoME 2022)
</i>
</p>
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