1 Fish detection based on spectral differences in the echogram's range and temporal domain Helge Balk and Torfinn Lindem University of Oslo Department of Physics / Proceeding for the FAST meeting in Bergen June 2003

2 Think of a fish-track in an echogram and - imagine the echo signal along a horizontal line - imagine the echo signal along a vertical line

3Material u Simrad EY500 u 120kHz 4x10 deg Split beam transducer u Sonar5-Pro post processing tool Simrad EY500Sonar5-Pro

4 Split beam echo sounder Phase detector Amplitude Detector 4-Ch TVG Single echo detector Position diagram SED-Echogram Amp-Echogram

5 Results Fish detection Conclude Single echo detector Principle Welcome Introduction Think of a fish Material and method Importance Cross-filter Cross-filter detector (CFD) Problems Human perception

6 Single echo detection (SED) is important in three situations a) for fish counting b) for fish behaviour study when fish echoes can be resolved as single targets

7 c) in abundance estimation of schools where the detections serve as a link between the fish and the Echo Integral (EI) u A link can be established by: u SED u Tracked SED u Catch statistics

8 Linking EI and SED to obtain the fish density SED detections surrounding a school gives the size distribution TS n Size distribution (n j ) School SED  =fish density j =Size class n =number in size class j N =all SED

9 The parametric single echo detector (SED) u Analyse one ping at a time u Describe a single echo with parameters u Echo length u Shape u Phase deviation u Angular position u Threshold

10 Two problems with SED 1. Rejection of echoes from single fish 2. Detection of false echoes from fluctuations in the background reverberation level Especially profound with low signal to noise ratio.

11 Results Fish detection Conclude Single echo detectors Principle Welcome Introduction Think of a fish Material and method Importance Cross-filter Cross-filter detector (CFD) Problems Differ from SED Human perception How to copy

12 Human perception extremely noisy, but we can still see the tracks Horizontal, stationary recording of spawning Bream

13 Human perception This is what the parametric SED evaluates This is what a parametric SED sees This is what we can see a) We look at more than one ping at a time. b) We apply information from the background

14 Next question- How can we copy these two elements in a computer algorithm? Answer: They can be copied with filters

15 Results Fish detection Conclude Single echo detector Principle Welcome Introduction Think of a fish Material and method Importance Cross-filter Cross-filter detector (CFD) filtering in time Problems Differ from SED Human perception Echogram freq. spectre filtering in range combining How to copy

16 Factors influencing on the echogram’s frequency components (mainly controllable factors) ship velocity ping rate sample rate beam width sound speed Range band width pulse length Time

17 An example from lake d’Annecy (Fr)

18 Filtering in time 1 Remove temporal noise pulses 2 Remove fluctuations in the fish track Combine information from multiple pings (mean filter) Missing echoes threshold Noise pulse Fish echo threshold Magnitude Freq. Frequency specter Energy from fish Energy from noise

19 Filtering in time improves the fish track

20 Filtering in time, equation and filter impulse response array F Q1Q1 H= [1/3 1/3 1/3]

21 Filtering in range A low-pass filter can remove the fish and detect the background signal Magnitude Freq. Frequency specter Energy from fish Energy from background

22 Filtering in range removes the fish Echogram FRange filtered echogram Q2

23 Combining the two results by letting Q2 define the threshold in Q1 Echograms F= Original Q 1 = Time filtered Q 2 = Range filtered Q 3 =Result

24 Combining the two filtered echograms Filtering in time H1 Filtering in range H2 Cross filter Named due to the orientation of the two filter impulse response arrays H 1 and H 2 Combining detector

25 Problems with false detections noisefishbottom/schools Fortunately the size of the detected regions differ size < size < size Size filter

26 Problems with echo quality threshold time filtered Faint echoes in a track can be detected in the process. Range will be correct Estimates based on phase such as TS and velocity may be unreliable Faint echoes time signal from a fish original threshold

27 Solution to the quality problem Mark each echo with a quality stamp A parametric “SED” can do this all TS and position Quality estimation Cross-filter detected echoes Tracking high quality Combines the best from the two detectors low quality high quality

28 Implementation of the Cross- filter detector in Sonar5-Pro software low quality high quality

29 Results Fish detection Conclude Single echo detector Principle Welcome Introduction Think of a fish Material and method Importance Cross-filter Cross-filter detector (CFD) filtering in time Problems Differ from SED Human perception Echogram Freq. spectre filtering in range combining How to copy Herring school Spawning Bream Migrating Salmon

30 Visual results, spawning Bream Amplitude echogram Parametric single echo detection Cross-filter detection before size filter Cross-filter detection after size filter

31 Visual results, herring school Rotate Time filter Range filter Combine Rotate + Size filter

32 Comparing thresholding, parametric SED and Cross-filtering Original Amp-echogramThresholded Amp-echogramParametric SED echogramCross-filtered SED-echogram

33 Numerical results D=Debris F=Fish B=Bottom Horizontal stationary recording River Tana summer 1999 Amp- echogram

34 Numerical results TQ Ratio between actual and possible number of detections TNR Track to noise ratio, number of detections in tracks rel total number of detections

35 Results Fish detection Conclude Single echo detector Principle Welcome Introduction Think of a fish Material and method Importance Cross-filter Cross-filter detector (CFD) filtering in time Problems Differ from SED Human perception Echogram Freq. spectre filtering in range combining How to copy Herring school Spawning Bream Migrating Salmon Acknowledgement Conclusion

36 Conclusions u The Cross-filter detector is a good alternative to the common parametric single echo detector u The Cross-filter detector is superior in situations with low SNR

37 Acknowledgment Data has been provided by….. Marie Prchalova (Cz) Horizontal recording of spawning Bream Nathalie Gaudreau (Ca) Vertical recording from Lake d’Annecy We thank all for their contribution! Frank R Knudsen at Simrad Assisted in the horizontal recording of salmon in River Tana Jim Vertical recording of Herring schools outside Vancouver island

38 Fish detection based on spectral differences in the echogram's range and temporal domain Test CD available by writing to... Helge Balk, Department of Physics PB1048, University of Oslo 0316 OSLO, NORWAY Thank you for your attention!