Krag, Ludvig Ahm

Lundgren, Bo

Madsen, Niels

Savina, Esther

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 21, 2017Developing a computer vision method to quantify impact on seabed of bottom gillnetsSavina, Esther; Lundgren, Bo; Krag, Ludvig Ahm; Madsen, NielsPublication date:2015Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Savina, E., Lundgren, B., Krag, L. A., & Madsen, N. (2015). Developing a computer vision method to quantifyimpact on seabed of bottom gillnets. Poster session presented at DEMaT’15, Aberdeen, United Kingdom.MATERIAL & METHODS Fig. 1. Stereo imaging recording unit  Two set-ups were experimented: (a) Type a: the cameras were mounted on top of the frame at a distance of 80 cm (b) Type b: the cameras were mounted in the frame at a distance of 65 cm and protected by netting to avoid entanglement of the net in the frame  Fig 4. Steps to process stereo imaging clips, adapted from Bradski and Kaehler (2008) and Neuswanger (2014) DATA COLLECTION AT SEA  Light and heavy commercial bottom set gillnets  Sandy bottoms, shallow waters  Stereo imaging recording unit (Fig. 1 and 2): cameras take synchronized images from slightly different perspectives and allow to estimate the distance to an object as in human vision  Simultaneous sea current measurement (Fig. 3) DATA ANALYSIS  Processing of stereo clips      (Fig. 4)   Statistical analysis   Fig 2. Experimental set-up for stereo imaging  (a) side view of a fleet, i.e., a ganged sequence of 3 individual gillnets, set on the bottom (b) top view of the stereo imaging recording unit positioned in front of a net  (c) underwater view of the floatline with red marks and rope to buoy Fig. 3. Drifting device to measure current speed and direction (a) full view of the device  (b) close-up view of the lower end of the PVC tube which allows to measure at the median net height in the water column (c) view of the device at sea Two similar devices were left drifting between the nets for short periods during soaking. Developing a computer vision method to quantify impact on seabed of bottom gillnets Esther Savina, Bo Lundgren, Ludvig A. Krag, Niels Madsen DEMaT’15 Aberdeen, UK  October 27-29, 2015 PhD student  +45 35 88 32 02 esav@aqua.dtu.dk Financial support CONTEXT AND CHALLENGES © http://ec.europa.eu © http://www.kidsafeseafood.org  Habitat damage is of high interest in an Ecosystem Approach to Fisheries  Seabed integrity is defined by the Marine Strategy Framework Directive as one of the descriptors to ensure Good Ecological Status    Develop an appropriate methodology for assessing the seabed impact of bottom gillnets  Assess the movement of the leadline of gillnet during soaking in 3-dimensions (x, y and z) OBJECTIVES OF THE STUDY  Effects of bottom set gillnets on habitat may be well below those associated with natural disturbance, but if located in areas of high biological importance, the effects on ecosystem functions could be larger    INTRODUCTION PRELIMINARY RESULTS  7 runs (Table 1) (Fig. 5 and 6)  Current during the experiment lower than the average range in coastal Danish waters (0.26 to 0.77m/s) (National Geospatial-Intelligence Agency, 2013)        EXPECTED OUTPUTS & NEXT STEPS  Test for significant differences between heavy and light nets, and correlate with current measurements  Look at seabed penetration of the leadline   Table 1. Experimental runs at sea  ‘Recording unit’ gives the type of set-up with ‘a’ for the type a (Fig. 1a) and ‘b’ for the type b (Fig. 1b). ‘Frame nr.’ gives the recording unit identification number. ‘Cameras’ gives the cameras identification numbers and their location in the recording pair with (L) for left and (R) for right. ‘Resolution’ gives the video resolution with ‘S’ standing for SuperView (the sides of the video are stretched out for greater viewing), ‘fps’ gives the frame per second and ‘FOV’ the field of view with ‘UW’ standing for Ultra Wide. Distorsion and calibration calculations were run in a tank with the same recording units and cameras as in the field, except for runs 1b and 1c where cameras 3(L)/4(R) and 11(L)/12(R), respectively, were used. Fig 5. Example of output from run 2c. Positions of 3 different marks (1, 2 and 3) on the leadline of the same net are recorded in the 3 dimensions (X, Y and Z) at different time intervals. The initial position is set to 0.     Fig 6. Boxplot of the maximum movement of the leadline for heavy and light nets in the 3 dimensions (X, Y and Z) calculated from all runs. RESULTS References  Bradski, G., Kaehler, A., 2008. Learning openCV: computer vision with the openCV library. O’Reilly Media. 555p. National Geospatial-Intelligence Agency of the United States Government, 2013. Sailing directions for Skagerrak and Kattegat. 14th ed., 168p. Neuswanger, J., 2014. New 3-d video methods reveal novel territorial drift-feeding behaviors that help explain environmental correlates of Chena river Chinook salmon productivity. PhD thesis, University of Alaska Fairbanks, 199p.  R Core Team (2015). R: A language and environment for statistical   computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.  (R Core Team, 2015) Run Date Location Current speed (m/s) Net Recording unit Frame nr. Cameras nr. GoPro Resolution fps FOV Clip length  1b 02-07 Hirtshals 0.17 (±0.11) light a 7 8(L)/13(R) 3 Black 1080p S 30 UW 64min 1c 02-07 Hirtshals  0.17 (±0.11) heavy a 4 3(L)/4(R) 3 Black 4K 12 UW 15min 2a 10-09 Strandby  0.10 (±0.09) heavy b 8 1(L)/2(R) 3+ Black 1080p S 30 UW 191min 2b 10-09 Strandby  0.10 (±0.09) light b 3 3(L)/4(R) 3+ Black 1080p S 30 UW 50min 2c 10-09 Strandby  0.10 (±0.09) heavy b 6 9(L)/10(R) 3+ Black 1080p S 30 UW 152min 3b 10-09 Strandby  0.10 (±0.09) light b 3 3(L)/4(R) 3+ Black 1080p S 30 UW 136min 3c 10-09 Strandby  0.10 (±0.09) heavy b 6 1(L)/2(R) 3+ Black 1080p S 30 UW 170min www.vidsync.org/HomePage 

Developing a computer vision method to quantify impact on seabed of bottom gillnets

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Developing a computer vision method to quantify impact on seabed of bottom gillnets

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