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Ron Vainshtein Itay Cohen Supervisors : Dr. Alon Amar, Yaakov Buchris In Collaboration with: Azriel Sinai Anomaly Detection in Multibeam Echosounder Seabed Scans
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Anomaly Detection in Multibeam Echosounder Seabed Scanssipl.eelabs.technion.ac.il/wp-content/uploads/sites/6/... · 2017. 7. 12. · Multibeam Scans •Echo sounding multibeam scanner.

Feb 18, 2021

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  • Ron Vainshtein

    Itay Cohen

    Supervisors:

    Dr. Alon Amar, Yaakov Buchris

    In Collaboration with: Azriel Sinai

    Anomaly Detection in Multibeam Echosounder Seabed Scans

  • Motivation

    • Multibeam seabed data.

    • Anomaly detection using scans.

    • Anomalies:• Mines• Pipes • Wreckage • Waste

    2/35

  • Multibeam Scans

    • Echo sounding multibeam scanner.• SeaBat T20P by Teledyne-Reson

    • 50 pings per second, 512 beams per ping

    • Seabed mapping via beam echo.

    • Scans affected by:• Wind

    • Waves

    • Vessel movement

    • Depth3/35

  • Scan Characteristics

    • Low resolution point cloud.

    • Scan lines pattern.

    • Spacing, direction and depth vary.

    • Sparse and not sampled uniformly.

    • Missing sample batches.4/35

  • Challenges

    • Scan analysis and processing is difficult.

    • Little prior information about targets.

    • Small scan dataset.

    • No prior work.

    Anomaly detection is challenging!

    5/35

  • Goals

    •Anomaly detection for multibeam seabed scans.

    •Overcome difficulties arising from the data.

    6/35

  • Scan Properties in Detail

    shore

    7/35

  • • Lines are spaced 0.2 - 0.7m.• Depends on vessel orientation.

    Line Spacing in Deep Water

    9/35

  • Targets in Deep Water

    11/35

  • Missing Data

    • Beams scattered away from scanner.• Scanner discards unreliable samples.

    scanner

    12/35

  • Algorithm Stages

    Holes Detection

    Filling Holes

    Anomaly Detection

    Multibeam Scan

    Regions of Interest

    Holes Detection

    13/35

  • Delaunay Tessellation Field Estimator• Used in cosmology.

    • Local density calculation.

    • Adaptive to variations in density and geometry.

    Holes Detection

    [Schaap & Van De Weygaert ’00]

    14/35

  • Delaunay Triangulation

    • Circumscribed circle contains only the inscribed triangle points.

    • Guarantees immediate neighbors.

    • Efficient O(nlog(n)).

    Holes Detection

    15/35

  • DTFE – Density Calculation

    • Take sum of areas of participating triangles.

    • Density at each vertex = 1

    sum.

    • Linearly interpolate density.

    Holes Detection

    16/35

  • DTFE-Based Hole Contouring

    • Apply DTFE and assign density to triangles.

    • Low density triangles indicate holes.

    • Create connected components from triangles.

    Holes Detection

    17/35

  • DTFE-Based Bole Contouring

    • Use threshold on area to avoid small holes.

    • Defines explicitly where to fix the data.

    Holes Detection

    18/35

  • Algorithm Stages

    Holes Detection

    Filling Holes

    Anomaly Detection

    Multibeam Scan

    Regions of Interest

    19/35

  • Filling Holes

    Defining the sampling points:• Sampling grid based on the triangulation and data.

    Choosing the Interpolation method:• Standard interpolation methods (linear, cubic, etc.) - poor reconstruction.

    • A multiscale iterative approach is used.

    Filling Holes

    20/35

  • Interpolation Method

    Laplacian Pyramid Extension

    • Pyramids – multiscale image manipulation.

    • Create stack of increasing scales from surroundings.• No downsampling is used.• Each scale is created using all previous scales.

    • Suitable for scattered data.

    Filling Holes

    [Bermanis, Coifman and Averbouch. ’13]

    21/35

  • Stack GenerationFilling Holes

    22/35

  • Filling Holes - ResultsFilling Holes

    23/35

  • Filling Holes – Deep WaterFilling Holes

    24/35

  • Algorithm Stages

    Holes Detection

    Filling Holes

    Anomaly Detection

    Multibeam Scan

    Regions of Interest

    25/35

  • ROI Detection

    >Threshold?

    26/35

  • ROI Detection

    • Variance map is calculated with several window sizes.

    • ROI decision – voting process on layers in patches.

    • Patch size and threshold are parameters.

    Anomaly DetectionAnomaly Detection

    27/35

  • Results – Target Anomaly Detection

    28/35

  • Results - Target

    ROI 1.6m

    Anomaly Detection

    29/35

  • Results - Background Anomaly Detection

    30/35

  • Results - Background

    ROI 1.6m

    Anomaly Detection

    31/35

  • Summary

    • Multibeam data is challenging

    • Algorithms from various fields

    • New interpolation framework

    • Innovative anomaly detection method

    32/35

  • • Will be used in new naval products.

    • Fits several seabed-related applications.

    • Publish a paper.

    • Develop anomaly classification algorithm.

    Achievements and Future Work

    34/35

  • Acknowledgments

    • Yaakov Buchris

    • Dr. Alon Amar

    • SIPL staff:• Nimrod Peleg

    • Yair Moshe

    • Ori Bryt

    • RAFAEL: Azriel Sinai

    35/35