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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Mizuno, K"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12338">arXiv:2411.12338</a> <span> [<a href="https://arxiv.org/pdf/2411.12338">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Target Height Estimation Using a Single Acoustic Camera for Compensation in 2D Seabed Mosaicking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xiaoteng Zhou</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yusheng Wang</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Katsunori Mizuno</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12338v1-abstract-short" style="display: inline;"> This letter proposes a novel approach for compensating target height data in 2D seabed mosaicking for low-visibility underwater perception. Acoustic cameras are effective sensors for sensing the marine environments due to their high-resolution imaging capabilities and robustness to darkness and turbidity. However, the loss of elevation angle during the imaging process results in a lack of target h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12338v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12338v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12338v1-abstract-full" style="display: none;"> This letter proposes a novel approach for compensating target height data in 2D seabed mosaicking for low-visibility underwater perception. Acoustic cameras are effective sensors for sensing the marine environments due to their high-resolution imaging capabilities and robustness to darkness and turbidity. However, the loss of elevation angle during the imaging process results in a lack of target height information in the original acoustic camera images, leading to a simplistic 2D representation of the seabed mosaicking. In perceiving cluttered and unexplored marine environments, target height data is crucial for avoiding collisions with marine robots. This study proposes a novel approach for estimating seabed target height using a single acoustic camera and integrates height data into 2D seabed mosaicking to compensate for the missing 3D dimension of seabed targets. Unlike classic methods that model the loss of elevation angle to achieve seabed 3D reconstruction, this study focuses on utilizing available acoustic cast shadow clues and simple sensor motion to quickly estimate target height. The feasibility of our proposal is verified through a water tank experiment and a simulation experiment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12338v1-abstract-full').style.display = 'none'; document.getElementById('2411.12338v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages,conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03564">arXiv:2408.03564</a> <span> [<a href="https://arxiv.org/pdf/2408.03564">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.marpolbul.2024.117030">10.1016/j.marpolbul.2024.117030 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Underwater litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+F">Fan Zhao</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yongying Liu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiaqi Wang</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yijia Chen</a>, <a href="/search/cs?searchtype=author&query=Xi%2C+D">Dianhan Xi</a>, <a href="/search/cs?searchtype=author&query=Shao%2C+X">Xinlei Shao</a>, <a href="/search/cs?searchtype=author&query=Tabeta%2C+S">Shigeru Tabeta</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Katsunori Mizuno</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03564v2-abstract-short" style="display: inline;"> Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current monitoring technologies for detecting underwater litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03564v2-abstract-full').style.display = 'inline'; document.getElementById('2408.03564v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03564v2-abstract-full" style="display: none;"> Underwater litter is widely spread across aquatic environments such as lakes, rivers, and oceans, significantly impacting natural ecosystems. Current monitoring technologies for detecting underwater litter face limitations in survey efficiency, cost, and environmental conditions, highlighting the need for efficient, consumer-grade technologies for automatic detection. This research introduces the Aerial-Aquatic Speedy Scanner (AASS) combined with Super-Resolution Reconstruction (SRR) and an improved YOLOv8 detection network. AASS enhances data acquisition efficiency over traditional methods, capturing high-quality images that accurately identify underwater waste. SRR improves image-resolution by mitigating motion blur and insufficient resolution, thereby enhancing detection tasks. Specifically, the RCAN model achieved the highest mean average precision (mAP) of 78.6% for detection accuracy on reconstructed images among the tested SRR models. With a magnification factor of 4, the SRR test set shows an improved mAP compared to the conventional bicubic set. These results demonstrate the effectiveness of the proposed method in detecting underwater litter. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03564v2-abstract-full').style.display = 'none'; document.getElementById('2408.03564v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The earlier version of this conference paper was accepted at OCEANS 2024-Halifax, Canada and was selected for inclusion in the Student Poster Competition (SPC) Program</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Marine Pollution Bulletin 209 (2024) 117030 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03559">arXiv:2408.03559</a> <span> [<a href="https://arxiv.org/pdf/2408.03559">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8 </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+F">Fan Zhao</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yijia Chen</a>, <a href="/search/cs?searchtype=author&query=Xi%2C+D">Dianhan Xi</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yongying Liu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiaqi Wang</a>, <a href="/search/cs?searchtype=author&query=Tabeta%2C+S">Shigeru Tabeta</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Katsunori Mizuno</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03559v1-abstract-short" style="display: inline;"> Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health, responding to climate change and pollution. Traditional survey methods, like quadrat sampling, are labor-intensive, time-consuming, and environmentally dependent. This study presents an innovative approach combining UAV-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03559v1-abstract-full').style.display = 'inline'; document.getElementById('2408.03559v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03559v1-abstract-full" style="display: none;"> Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds, cleaning up debris, and disturbing soil. They serve as vital indicators of marine environmental health, responding to climate change and pollution. Traditional survey methods, like quadrat sampling, are labor-intensive, time-consuming, and environmentally dependent. This study presents an innovative approach combining UAV-based remote sensing with Super-Resolution Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of YOLOv8s, to monitor hermit crabs. SRR enhances image quality by addressing issues such as motion blur and insufficient resolution, significantly improving detection accuracy over conventional low-resolution fuzzy images. The CRAB-YOLO network integrates three improvements for detection accuracy, hermit crab characteristics, and computational efficiency, achieving state-of-the-art (SOTA) performance compared to other mainstream detection models. The RDN networks demonstrated the best image reconstruction performance, and CRAB-YOLO achieved a mean average precision (mAP) of 69.5% on the SRR test set, a 40% improvement over the conventional Bicubic method with a magnification factor of 4. These results indicate that the proposed method is effective in detecting hermit crabs, offering a cost-effective and automated solution for extensive hermit crab monitoring, thereby aiding coastal benthos conservation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03559v1-abstract-full').style.display = 'none'; document.getElementById('2408.03559v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The earlier version of this conference paper was presented at OCEANS 2024-Singapore and was selected for inclusion in the Student Poster Competition (SPC) Program</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.02914">arXiv:2406.02914</a> <span> [<a href="https://arxiv.org/pdf/2406.02914">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhou%2C+X">Xiaoteng Zhou</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Katsunori Mizuno</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yilong Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.02914v1-abstract-short" style="display: inline;"> In low-visibility marine environments characterized by turbidity and darkness, acoustic cameras serve as visual sensors capable of generating high-resolution 2D sonar images. However, acoustic camera images are interfered with by complex noise and are difficult to be directly ingested by downstream visual algorithms. This paper introduces a novel strategy for denoising acoustic camera images using… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02914v1-abstract-full').style.display = 'inline'; document.getElementById('2406.02914v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02914v1-abstract-full" style="display: none;"> In low-visibility marine environments characterized by turbidity and darkness, acoustic cameras serve as visual sensors capable of generating high-resolution 2D sonar images. However, acoustic camera images are interfered with by complex noise and are difficult to be directly ingested by downstream visual algorithms. This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques, which comprises two principal components: a self-supervised denoising framework and a fine feature-guided block. Additionally, the study explores the relationship between the level of image denoising and the improvement in feature-matching performance. Experimental results show that the proposed denoising strategy can effectively filter acoustic camera images without prior knowledge of the noise model. The denoising process is nearly end-to-end without complex parameter tuning and post-processing. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02914v1-abstract-full').style.display = 'none'; document.getElementById('2406.02914v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.12756">arXiv:2205.12756</a> <span> [<a href="https://arxiv.org/pdf/2205.12756">pdf</a>, <a href="https://arxiv.org/format/2205.12756">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Development of a Stereo-Vision Based High-Throughput Robotic System for Mouse Tail Vein Injection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ko%2C+T">Tianyi Ko</a>, <a href="/search/cs?searchtype=author&query=Nishiwaki%2C+K">Koichi Nishiwaki</a>, <a href="/search/cs?searchtype=author&query=Terada%2C+K">Koji Terada</a>, <a href="/search/cs?searchtype=author&query=Tanaka%2C+Y">Yusuke Tanaka</a>, <a href="/search/cs?searchtype=author&query=Mitsumata%2C+S">Shun Mitsumata</a>, <a href="/search/cs?searchtype=author&query=Katagiri%2C+R">Ryuichi Katagiri</a>, <a href="/search/cs?searchtype=author&query=Junko%2C+T">Taketo Junko</a>, <a href="/search/cs?searchtype=author&query=Horiba%2C+N">Naoshi Horiba</a>, <a href="/search/cs?searchtype=author&query=Igata%2C+H">Hideyoshi Igata</a>, <a href="/search/cs?searchtype=author&query=Mizuno%2C+K">Kazue Mizuno</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.12756v1-abstract-short" style="display: inline;"> In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12756v1-abstract-full').style.display = 'inline'; document.getElementById('2205.12756v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.12756v1-abstract-full" style="display: none;"> In this paper, we present a robotic device for mouse tail vein injection. We propose a mouse holding mechanism to realize vein injection without anesthetizing the mouse, which consists of a tourniquet, vacuum port, and adaptive tail-end fixture. The position of the target vein in 3D space is reconstructed from a high-resolution stereo vision. The vein is detected by a simple but robust vein line detector. Thanks to the proposed two-staged calibration process, the total time for the injection process is limited to 1.5 minutes, despite that the position of needle and tail vein varies for each trial. We performed an injection experiment targeting 40 mice and succeeded to inject saline to 37 of them, resulting 92.5% success ratio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.12756v1-abstract-full').style.display = 'none'; document.getElementById('2205.12756v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted to ICRA2022 (7 pages, 11 figures, 2 tables)</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> 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