SOLAQUA: SINTEF Ocean Large Aquaculture Robotics Dataset
- URL: http://arxiv.org/abs/2504.01790v2
- Date: Mon, 28 Apr 2025 18:57:06 GMT
- Title: SOLAQUA: SINTEF Ocean Large Aquaculture Robotics Dataset
- Authors: Sveinung Johan Ohrem, Bent Haugaløkken, Eleni Kelasidi,
- Abstract summary: This paper presents a dataset gathered with an underwater robot in a sea-based aquaculture setting.<n>Data was gathered from sensors such as the Waterlinked A50 DVL, the Nortek Nucleus 1000 DVL, Sonardyne Micro Ranger 2 USBL, Sonoptix Mulitbeam Sonar, mono and stereo cameras, and vehicle sensor data such as power usage, IMU, pressure, temperature, and more.<n>It is expected that both the research community and the aquaculture industry will benefit greatly from the utilization of the proposed SOLAQUA dataset.
- Score: 1.2289361708127877
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This paper presents a dataset gathered with an underwater robot in a sea-based aquaculture setting. Data was gathered from an operational fish farm and includes data from sensors such as the Waterlinked A50 DVL, the Nortek Nucleus 1000 DVL, Sonardyne Micro Ranger 2 USBL, Sonoptix Mulitbeam Sonar, mono and stereo cameras, and vehicle sensor data such as power usage, IMU, pressure, temperature, and more. Data acquisition is performed during both manual and autonomous traversal of the net pen structure. The collected vision data is of undamaged nets with some fish and marine growth presence, and it is expected that both the research community and the aquaculture industry will benefit greatly from the utilization of the proposed SOLAQUA dataset.
Related papers
- The Marine Debris Forward-Looking Sonar Datasets [10.878811189489804]
This paper presents the Marine Debris Forward-Looking Sonar datasets, with three different settings.
We provide full dataset description, basic analysis and initial results for some tasks.
We expect the research community will benefit from this dataset.
arXiv Detail & Related papers (2025-03-28T21:12:03Z) - Prediction Model of Aqua Fisheries Using IoT Devices [0.6526824510982799]
This thesis proposes an IoT-based framework using sensors and Arduino for efficient monitoring and control of water quality.<n>Different sensors including pH, temperature, and turbidity are placed in cultivating pond water and each of them is connected to a common microcontroller board.<n>The sensors read the data from the water and store it as a CSV file in an IoT cloud named Thingspeak through the Arduino Microcontroller.
arXiv Detail & Related papers (2025-01-11T13:46:10Z) - Underwater Camouflaged Object Tracking Meets Vision-Language SAM2 [60.47622353256502]
We propose the first large-scale multi-modal underwater camouflaged object tracking dataset, namely UW-COT220.<n>Based on the proposed dataset, this work first evaluates current advanced visual object tracking methods, including SAM- and SAM2-based trackers, in challenging underwater environments.<n>Our findings highlight the improvements of SAM2 over SAM, demonstrating its enhanced ability to handle the complexities of underwater camouflaged objects.
arXiv Detail & Related papers (2024-09-25T13:10:03Z) - ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics [14.935296890629795]
Oysters are a vital keystone species in coastal ecosystems, providing significant economic, environmental, and cultural benefits.<n>Current monitoring strategies often rely on destructive methods.<n>We propose a novel pipeline using stable diffusion to augment a collected real dataset with realistic synthetic data.
arXiv Detail & Related papers (2024-09-11T04:31:09Z) - SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles [10.732732686425308]
This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation.<n>It focuses on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs)
arXiv Detail & Related papers (2024-04-29T04:00:19Z) - Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies
mapping [40.996860106131244]
Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability.
This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions.
arXiv Detail & Related papers (2024-01-05T18:11:08Z) - Improving Underwater Visual Tracking With a Large Scale Dataset and
Image Enhancement [70.2429155741593]
This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT)
It poses distinct challenges; the underwater environment exhibits non-uniform lighting conditions, low visibility, lack of sharpness, low contrast, camouflage, and reflections from suspended particles.
We propose a novel underwater image enhancement algorithm designed specifically to boost tracking quality.
The method has resulted in a significant performance improvement, of up to 5.0% AUC, of state-of-the-art (SOTA) visual trackers.
arXiv Detail & Related papers (2023-08-30T07:41:26Z) - Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol
Particles for Frontier Exploration [55.41644538483948]
This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles.
It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format.
The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
arXiv Detail & Related papers (2023-04-27T20:21:18Z) - Learning-based estimation of in-situ wind speed from underwater
acoustics [58.293528982012255]
We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics.
Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
arXiv Detail & Related papers (2022-08-18T15:27:40Z) - Deep Reinforcement Learning Assisted Federated Learning Algorithm for
Data Management of IIoT [82.33080550378068]
The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment.
How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue.
This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
arXiv Detail & Related papers (2022-02-03T07:12:36Z) - Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and
On-Device Inference [49.88536971774444]
Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.
Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services.
We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
arXiv Detail & Related papers (2020-01-13T04:41:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.