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zenodo44/100

Fusion of Underwater Camera and Multibeam Sonar for Diver Detection and Tracking

<div>Context</div> <div> </div> <div>This dataset is related to previously published public dataset "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments". <a href="../records/7728089">https://zenodo.org/records/7728089</a></div> <div>It contains ZED-right camera and sonar images collected from Hemmoor Lake and DFKI Maritime Exploration Hall.</div> <div> </div> <div>Sensors: Low Frq (1.2MHz) Blueprint Oculus M1200d Sonar and ZED Right Camera</div> <div> </div> <div>Content</div> <div> </div> <div>The dataset is created for Diver Detection and Diver Tracking applications.</div> <div> </div> <div>For the Diver Detection part, the dataset is prepared to train, validate and test YOLOv7 model.</div> <div>7095 images are used for training data, and 3095 images are used for validation data. These sets are augmented from originally captured and sampled ZED camera images. Augmentation methods are not applied to the Test data, which contains 822 images. Train and validation contain images from both the DFKI pool and Hemmor Lake, while the test data is only collected from the lake.</div> <div> </div> <div>To distinguish between the original image and the augmented image, check the name coding. </div> <div>Naming of object detection images:</div> <div>original_image_name.jpg</div> <div>if augmented:</div> <div>original_image_name_<augmentation_number_of_the_same_image>.jpg</div> <div> </div> <div>Object Detection Label Format: </div> <div>YOLO [(class), ((x_min + (x_max - x_min)/2)  / image_width), ((y_min + (y_max - y_min)/2)  / image_height), ((x_max - x_min) / image_width), ((y_max - y_min) / image_height)]</div> <div> </div> <div>Class: "diver", represented by "0" in object detection labels.</div> <div> </div> <div>Resolution of Object Detection Camera Images: 640x640</div> <div>Resolution of Object Tracking Camera Images: 1280x720</div> <div>Resolution of Object Tracking Low Frequency Sonar: 932x514</div> <div> </div> <div>About the Object Tracking on Sonar, the sampled data is the part where diver moves around the table and the platform. </div> <div>There are 4 cases shared in the dataset, which contain a sonar stream, and corresponding ZED-right camera images. </div> <div>Totally, 1193 points represent the diver on sonar images for the diver tracking application.</div> <div> </div> <div>For the tracking, "tracking_sonar_coordinates_<number>.csv" contains x,y coordinates of a point where the diver is in the sonar image. </div> <div>And "image_sonar_<number>.csv" file contains the matching between sonar and camera images.</div> <div> </div> <div>Acknowledgements</div> <div> </div> <div>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</div>

opencc-by-4.0Dec 2023View details →

zenodo44/100

Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation

<h1>Metadata of a Large Sonar and Stereo Camera Dataset Suitable for Sonar-to-RGB Image Translation</h1> <h2>Introduction</h2> This is a set of metadata describing a large dataset of synchronized sonar and stereo camera recordings, that were captured between August 2021 and September 2023 during the project <a href="https://robotik.dfki-bremen.de/en/research/projects/deepersense/">DeeperSense</a> (https://robotik.dfki-bremen.de/en/research/projects/deepersense/), as training data for Sonar-to-RGB image translation. <a href="../records/7728089">Parts</a> <a href="../records/10220989">of</a> the sensor data have been published (https://zenodo.org/records/7728089, https://zenodo.org/records/10220989). Due to the size of the sensor data corpus, it is currently impractical to make the entire corpus accessible online. Instead, this metadatabase serves as a relatively compact representation, allowing interested researchers to inspect the data, and select relevant portions for their particular use case, which will be made available on demand. This is an effort to comply with the <a href="https://www.go-fair.org/fair-principles/">FAIR</a> principle A2 (https://www.go-fair.org/fair-principles/) that metadata shall be accessible, even when the base data is not immediately. <h3>Locations and sensors</h3> The sensor data was captured at four different locations, including one laboratory (Maritime Exploration Hall at DFKI RIC Bremen) and three field locations (Chalk Lake Hemmoor, Tank Wash Basin Neu-Ulm, Lake Starnberg). At all locations, a ZED camera and a Blueprint Oculus M1200d sonar were used. Additionally, a SeaVision camera was used at the Maritime Exploration Hall at DFKI RIC Bremen and at the Chalk Lake Hemmoor. The <code>examples/</code> directory holds a typical output image for each sensor at each available location. <h3>Data volume per session</h3> Six data collection sessions were conducted. The table below presents an overview of the amount of data captured in each session: <table> <tbody> <tr> <th>Session dates</th> <th>Location</th> <th>Number of datasets</th> <th>Total duration of datasets [h]</th> <th>Total logfile size [GB]</th> <th>Number of images</th> <th>Total image size [GB]</th> </tr> <tr> <td>2021-08-09 - 2021-08-12</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>52</td> <td>10.8</td> <td>28.8</td> <td>389’047</td> <td>88.1</td> </tr> <tr> <td>2022-02-07 - 2022-02-08</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>35</td> <td>4.4</td> <td>54.1</td> <td>629’626</td> <td>62.3</td> </tr> <tr> <td>2022-04-26 - 2022-04-28</td> <td>Chalk Lake Hemmoor</td> <td>52</td> <td>8.1</td> <td>133.6</td> <td>1’114’281</td> <td>97.8</td> </tr> <tr> <td>2022-06-28 - 2022-06-29</td> <td>Tank Wash Basin Neu-Ulm</td> <td>42</td> <td>6.7</td> <td>144.2</td> <td>824’969</td> <td>26.9</td> </tr> <tr> <td>2023-04-26 - 2023-04-27</td> <td>Maritime Exploration Hall at DFKI RIC Bremen</td> <td>55</td> <td>7.4</td> <td>141.9</td> <td>739’613</td> <td>9.6</td> </tr> <tr> <td>2023-09-01 - 2023-09-02</td> <td>Lake Starnberg</td> <td>19</td> <td>2.9</td> <td>40.1</td> <td>217’385</td> <td>2.3</td> </tr> <tr> <th> </th> <th> </th> <th>255</th> <th>40.3</th> <th>542.7</th> <th>3’914’921</th> <th>287.0</th> </tr> </tbody> </table> <h2>Data and metadata structure</h2> <h3>Sensor data corpus</h3> The sensor data corpus comprises two processing stages: <ul> <li>raw data streams stored in ROS bagfiles (aka logfiles),</li> <li>camera and sonar images (aka datafiles) extracted from the logfiles.</li> </ul> The files are stored in a file tree hierarchy which groups them by session, dataset, and modality: <pre><code>${session_key}/ ${dataset_key}/ ${logfile_name} ${modality_key}/ ${datafile_name}</code></pre> A typical logfile path has this form: <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ stereo_camera-zed-2023-09-02-15-06-07.bag</code></pre> A typical datafile path has this form: <pre><code>2023-09_starnberg_lake/ 2023-09-02-15-06_hydraulic_drill/ zed_right/ 1693660038_368077993.jpg</code></pre> All directory and file names, and their particles, are designed to serve as identifiers in the metadatabase. Their formatting, as well as the definitions of all terms, are documented in the file <code>entities.json</code>. <h3>Metadatabase</h3> The metadatabase is provided in two equivalent forms: <ul> <li>as a standalone <a href="https://www.sqlite.org/index.html">SQLite</a> (https://www.sqlite.org/index.html) database file <code>metadata.sqlite</code> for users familiar with SQLite,</li> <li>as a collection of CSV files in the <code>csv/</code> directory for users who prefer other tools.</li> </ul> The database file has been generated from the CSV files, so each database table holds the same information as the corresponding CSV file. In addition, the metadatabase contains a series of convenience views that facilitate access to certain aggregate information. An entity relationship diagram of the metadatabase tables is stored in the file <code>entity_relationship_diagram.png</code>. Each entity, its attributes, and relations are documented in detail in the file <code>entities.json</code> Some general design remarks: <ul> <li>For convenience, timestamps are always given in both a human-readable form (ISO 8601 formatted datetime strings with explicit local time zone), and as seconds since the UNIX epoch.</li> <li>In practice, each logfile always contains a single stream, and each stream is stored always in a single logfile. Per database schema however, the entities <code>stream</code> and <code>logfile</code> are modeled separately, with a “many-streams-to-one-logfile” relationship. This design was chosen to be compatible with, and open for, data collections where a single logfile contains multiple streams.</li> <li>A <code>modality</code> is not an attribute of a <code>sensor</code> alone, but of a <code>datafile</code>: Because a <code>sensor</code> is an attribute of a <code>stream</code>, and a single stream may be the source of multiple modalities (e.g. RGB vs. grayscale images from the same camera, or cartesian vs. polar projection of the same sonar output). Conversely, the same modality may originate from different sensors.</li> </ul> As a usage example, the data volume per session which is tabulated at the top of this document, can be extracted from the metadatabase with the following SQL query: <div> <pre><code>SELECT PRINTF( '%s - %s', SUBSTR(session_start, 1, 10), SUBSTR(session_end, 1, 10)) AS 'Session dates', location_name_english AS Location, number_of_datasets AS 'Number of datasets', total_duration_of_datasets_h AS 'Total duration of datasets [h]', total_logfile_size_gb AS 'Total logfile size [GB]', number_of_images AS 'Number of images', total_image_size_gb AS 'Total image size [GB]' FROM location JOIN session USING (location_id) JOIN ( SELECT session_id, COUNT(dataset_id) AS number_of_datasets, ROUND( SUM(dataset_duration) / 3600, 1) AS total_duration_of_datasets_h, ROUND( SUM(total_logfile_size) / 10e9, 1) AS total_logfile_size_gb FROM location JOIN session USING (location_id) JOIN dataset USING (session_id) JOIN view__dataset_total_logfile_size USING (dataset_id) GROUP BY session_id ) USING (session_id) JOIN ( SELECT session_id, COUNT(datafile_id) AS number_of_images, ROUND(SUM(datafile_size) / 10e9, 1) AS total_image_size_gb FROM session JOIN dataset USING (session_id) JOIN stream USING (dataset_id) JOIN datafile USING (stream_id) GROUP BY session_id ) USING (session_id) ORDER BY session_id;</code></pre> </div>

opencc-by-4.0Dec 2023View details →

zenodo36/100

AUV-Based Multi-Sensor Dataset: Forward-Looking Camera (FLC) and Forward-Looking Sonar (FLS) Observations in the Red Sea

Context This dataset is the first part of a dataset collection comprised of forward-looking sonar (FLS) and forward-looking camera (FLC) underwater images. The entire data was collected during the years 2021-2023 using 2 underwater vehicles in both the Red Sea and the Mediterranean along the Israeli shoreline, depicting both man-made and natural underwater environments. The data is part of a research project aimed at developing fusion models for improved obstacle detection and navigation in autonomous underwater vehicles. Content This dataset consists of FLC and FLS images and their metadata, collected by the ALICE-AUV. Both sensors were installed in the front payload section in a configuration having aligned fields of view to achieve matching pairs of data. The data was collected to train and evaluate a complete perception and obstacle avoidance framework. A series of diving sessions were performed in the Red Sea, off the coast of Eilat, Israel. The experiments focused on two main sites: A "Sunboat" shipwreck and the Eilat-Ashkelon Pipeline Company (EAPC) pier pillars. The "Sunboat" shipwreck is a 40-meter long vessel resting at a depth of approximately 12 meters, with the surrounding seabed at a depth of 18-24 meters. This dataset contains approximately 8,000 FLC-FLS sample pairs from the first session conducted at the "Sun boat" shipwreck site on September 3, 2023. The data was recorded at depths ranging from 10 to 15 meters. The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, the data is further categorized into modalities: camera (FLC images), sonar (FLS images), and navigation (dead reckoning data). The navigation data is derived from a combination of GPS, DVL, and IMU sensors, providing estimated positions when GPS is unavailable. Inside each modality directory, you will find the corresponding data files in PNG format for images and CSV format for navigation data. The file names follow a sequential numbering scheme (e.g., 00001.png, 00002.png, etc.). Each modality directory also contains a CSV file (e.g., camera.csv) that maps each data file to its respective timestamp. Additionally, the samples.json file documents the relationship between uni-modal and multi-modal samples, allowing for easy association of data from different modalities. By providing synchronized and aligned camera and sonar imagery, along with corresponding navigation data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles. Technical Details <ul> <li>Sonar: Blueprint Oculus M1200d <ul> <li>Operating frequency: 1.2 MHz (low frequency mode)</li> <li>Maximum range: 40 m (set to 20 m for this dataset)</li> <li>Horizontal aperture: 130°</li> <li>Vertical aperture: 20°</li> <li>Number of beams: 512</li> <li>Angular resolution: 0.6°</li> <li>Beam separation: 0.25°</li> <li>Image resolution: 902x497 pixels</li> <li>Coordinate system: Polar</li> </ul> </li> <li>Camera: Allied-Vision Manta G-917 <ul> <li>Image dimensions: 3384x2710 pixels (downscaled to 1692x1355 for this dataset)</li> <li>Sensor type: CCD Progressive</li> <li>Sensor bit depth: 12-bit</li> <li>Captured bit depth: 8-bit</li> <li>Camera model: Pinhole with Plumb Bob (Brown–Conrady) distortion coefficients</li> <li>Focal length (fx, fy): (1638.36157, 1641.95202)</li> <li>Principal point (cx, cy): (1705.03529, 1380.27954)</li> <li>Radial distortion coefficients (k1, k2, k3): (-0.124823, 0.048851, 0.000000)</li> <li>Tangential distortion coefficients (p1, p2): (0.000259, -0.002945)</li> </ul> </li> <li>Navigation: <ul> <li>Data format: CSV</li> <li>Contains fused dead reckoning data based on GPS, DVL, and IMU sensors</li> <li>Columns: <ul> <li>timestamp: Unix timestamp (seconds)</li> <li>latitude: Latitude (degrees)</li> <li>longitude: Longitude (degrees)</li> <li>altitude: Altitude (meters)</li> <li>yaw: Yaw angle (degrees)</li> <li>pitch: Pitch angle (degrees)</li> <li>roll: Roll angle (degrees)</li> <li>velocity_x: Velocity along the x-axis (meters per second)</li> <li>velocity_y: Velocity along the y-axis (meters per second)</li> <li>velocity_z: Velocity along the z-axis (meters per second)</li> <li>depth: Depth (meters)</li> </ul> </li> </ul> </li> <li>Frame rate: 2 Hz for both sonar and camera</li> </ul> More datasets from this collection will be uploaded in the future, and a link to access them will be provided on this page. Acknowledgements The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.

opencc-by-4.0Jan 2024View details →

zenodo28/100

ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat

Description: This dataset consists of approximately 46,928 synchronized image pairs collected by the  Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ)  and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap. The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.   Context: This dataset is the second installment in our collection of synchronized multi-sensor underwater  datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle  detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected  using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea,  off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a  research project focused on developing fusion models for improved obstacle detection and  navigation in AUVs.   Content: The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including  corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was  operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths  ranging from 3 to 12 meters at different times from dawn to dusk.   Dataset Composition: <div> <table> <tbody> <tr> <td> Site </td> <td> Recording Session </td> <td> Image Pairs </td> <td> Description </td> </tr> <tr> <td> Tropical Site 1 </td> <td> 20221211_092506 20221211_133252 </td> <td> 10,915 7,978 </td> <td> Pier, rocks, corals </td> </tr> <tr> <td> Tropical Site 2 </td> <td> 20221212_095821 20221212_141308 </td> <td> 9,900 8,475 </td> <td> Man-made structure,  rocks, corals </td> </tr> <tr> <td> Tropical Site 3 </td> <td> 20221213_102542 </td> <td> 9,390 </td> <td> Rocks, corals </td> </tr> <tr> <td> Total </td> <td> </td> <td> 46,928  </td> <td> </td> </tr> </tbody> </table> </div>   The dataset is organized into separate sessions, each representing a specific dive or  experiment. Within each session, data is further categorized into modalities: camera (FLC  images), sonar (FLS images), and depth. Each modality directory contains the corresponding  data files in PNG format for images and CSV format for depth data.   Each modality directory includes: <ul> <li> A `camera.csv` file for the camera modality that maps each image file to its respective  timestamp. </li> <li> A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp. </li> <li> The depth data in `depth.csv` formatted with `timestamp` and `value`. </li> </ul> Additionally, a `samples.json` file documents the relationship between uni-modal and  multi-modal samples, enabling easy association of data from different modalities.   Technical Details: <ul> <li> Camera: IDS UI-3260CP-C-HQ </li> <ul> <li> Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset) </li> <li> Sensor type: Sony IMX249 1/1.2" CMOS </li> <li> Lens: Tamron M112FM06 </li> <li> Captured bit depth: 8-bit </li> <li> Frame rate: 5 Hz </li> </ul> <li> Sonar: BluePrint Oculus M1200d </li> <ul> <li> Operating frequency: 1.2 MHz (low frequency mode) </li> <li> Maximum range: 40 m (set to 15 m for this dataset) </li> <li> Horizontal aperture: 130° </li> <li> Vertical aperture: 20° </li> <li> Number of beams: 512 </li> <li> Angular resolution: 0.6° </li> <li> Beam separation: 0.25° </li> <li> Image resolution: 544x300 pixels </li> <li> Coordinate system: Polar </li> <li> Frame rate: 5 Hz </li> </ul> <li> Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder </li> <ul> <li> Frequency: 115 kHz </li> <li> Source Level: 198 dB re 1µPa @ 1m </li> <li> Beamwidth: 25 degrees </li> <li> Typical Minimum Range: 0.3 m (1 ft) </li> <li> Typical Usable Range: 100 m (328 ft) </li> <li> Range Resolution: 0.5% of range </li> <li> Depth Rating: 300 m (984 ft) </li> <li> Data format: CSV </li> <li> Columns: </li> <ul> <li> timestamp: Unix timestamp (seconds) </li> <li> value: Depth value (meters) </li> </ul> <li> Sample rate: 5 Hz </li> </ul> </ul>   Example File Tree Layout: ``` ${session}/ ${dataset}/ camera/ camera.csv 00000001.png 00000002.png … sonar/ sonar.csv 00000001.png 00000002.png … depth/ depth.csv samples.json ``` Example File Content:  camera.csv ``` timestamp,filename 1644234340.181234,00000001.png 1644234343.375667,00000002.png ```  sonar.csv ``` timestamp,filename 1644234340.181234,00000001.png 1644234343.375667,00000002.png ```  depth.csv ``` timestamp,value 1644234340.181234,5.4 1644234343.375667,6.1 ```  samples.json ``` {     "samples": [         {             "camera": [                 0             ],             "depth": [                 0             ],             "sonar": [                 0             ]         },         {             "camera": [                 1             ],             "depth": [                 1             ],             "sonar": [                 1             ]         } ] ``` By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset  enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in  the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea. Acknowledgements The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.

opencc-by-4.0Jun 2024View details →

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