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4 results for “Multibeam Sonar”
Fusion of Underwater Camera and Multibeam Sonar for Diver Detection and Tracking
<div><strong>Context</strong></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><strong>Content</strong></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><strong>Acknowledgements</strong></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> <p> </p>
Data from: Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada
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Data from: Reliability of fish size estimates obtained from a multibeam imaging sonar
Multibeam imaging sonars have considerable potential for use in fisheries surveys because the video-like images are easy to interpret and contain information about fish size, shape, and swimming behavior, as well as characteristics of occupied habitats. We examined images obtained using a DIDSON multibeam sonar for Atlantic sturgeon Acipenser oxyrinchus, striped bass Morone saxatilis, white perch M. americana, and channel catfish Ictalurus punctatus of known size (20 - 141 cm) to determine the reliability of length estimates. For ranges up to 11 m, percent measurement error (sonar estimate - total length)/total length X 100 varied by species but was not related to the fish's range or aspect angle (orientation relative to the sonar beam). Least-square mean percent error was significantly different from 0.0 for Atlantic sturgeon (x ̅=-8.34, SE=2.39) and white perch (x ̅=14.48, SE=3.99) but not striped bass (x ̅= 3.71, SE=2.58) or channel catfish (x ̅=3.97, SE=5.16). Underestimating lengths of Atlantic sturgeon may be due to difficulty in detecting the snout or the longer dorsal lobe of the heterocercal tail. White perch was the smallest species tested and it had the largest percent measurement errors (both positive and negative) and the lowest percentage of images classified as good or acceptable. Automated length estimates using Echoview software varied with position in the view-field. Estimates tended to be low at more extreme azimuthal angles (fish's angle off-axis within the view-field) but mean and maximum estimates were highly correlated with total length. Software estimates were also biased by fish images partially outside the view-field and when acoustic crosstalk occurred (when a fish perpendicular to the sonar and at relatively close range is detected in the side lobes of adjacent beams). These sources of bias are apparent when files are processed manually and can be filtered out when producing automated software estimates. Multibeam sonar estimates of fish size should be useful for research and management if these potential sources of bias and imprecision are addressed.
Data from: Reliability of fish size estimates obtained from a multibeam imaging sonar
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