1 / 16

Improved fish detection probability in data from split-beam sonars.

Improved fish detection probability in data from split-beam sonars. Helge Balk and Torfinn Lindem. Department of Physics. University of Oslo. Method and material. Development of sonar software. Most data collected with Simrad EY500. Some data collected with HTI modell-243

teal
Download Presentation

Improved fish detection probability in data from split-beam sonars.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Improved fish detection probability in data from split-beam sonars. Helge Balk and Torfinn Lindem. Department of Physics. University of Oslo.

  2. Method and material. • Development of sonar software. • Most data collected with Simrad EY500. • Some data collected with HTI modell-243 • Experience from fieldwork. • River Tornio (Finland summer 97) • Lake Semsvannet. (Norway winter 98, 99) • River Tana (Norway summer 98, 99) • (Data from various other rivers and lakes.)

  3. 4-Ch TVG Traditional counting method. Single Echo Detector (SED) Tracking Phase detector Envelope Detector fish-track track statistics split-beam transducer SED-echogram (time / range) Raw-echogram (time / range) X/Y position diagram

  4. Horizontal application in shallow rivers: • The traditional method tends to faile because of: • Increased noise-level. • (Rain, silt, running water, bottom and surface reflections, air-bubles, debris ) • Increased phase and amplitude fluctuations in the echo-signal. • ( side-aspect, reflection, transducer-vibrations, moving sound-media ) • Multiple object problem. • (fish, debris, stones....;- Classification nessesary)

  5. SED;- the main problem. Classification Tracking SED • Noise is too easily detected as single targets. • Important information is removed. 1) Increased fluctuation in the echo-signal increases the rejection of echoes from fish. 2) The shape of a track. 3) Echoes below a fixed treshold. 4) Echoes from fish-schools. RAW-Echogram SED-Echogram

  6. Tracking algorithm. Classification Tracking SED a) Missing echoes results in rejection of fish-tracks. b) Noise-echoes results in creations of artificial fish-like tracks. Tracking result SED-echogram Four salmons a rainy day in Tana.

  7. How to improve the method. Collect more data. Extract more information from existing data. Classification Tracking SED Four salmons on a rainy day in Tana. SED-echogram Raw-echogram

  8. ? ?

  9. Image Texture Classification Intensity Shape analysis Image analysis. Filters Contour detection Segmentation Morphologic operations

  10. Convolution and window operations. Echogram array F(m1, m2) Window producing one output pixel. I H G F E D C B A I H G F E D C B A 0 1 0 1-4 1 0 1 0 F (n1, n2 ) = Input image array H(m1, m2 ) = Impulse response array Q(m1,m2) = Output image array 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 -1-1-1 Hit-miss, mean, roberts-c, laplace

  11. Filtering. • Many well-known filters availabel. • Low-pass: Median, Mean, Knn, Sigma, • High-Pass: Sobel, Robert’s, Prewitt, Gradient, Laplace. • Morphologic filters: Hit-miss, Hit-add. • Not always an improvement. • Filter dimension is important. Original Median 9x1 Median 1x9 Robert’s col. Robert’s col. Echogram Low-pass Low-pass High-pass + Median 3x5

  12. Segmentation. Separating background and foreground. • Edgebased. • Detecting edges. • (high-pass: gradient, Laplace.) • Linking. • Region based. • Tresholding. • Growing and shrinking. • Seeds. • Split and merge. • Relaxation.

  13. Shape analysis. • Central moments. • Radius of gyration. • Orientation. • Topological features. • Area. • Contour length and smoothness. • Compactness. • Eccentricity. • Small shapes originates from surface noise and airbubbles. • High fluctuation in contour and large area may indicate bottom structures. • Fish and debris seen to produce thin and smooth tracks.

  14. Raw- echogram Putting things together. Phase detector Single Echo Detector Contour detector Image processor Regions of fish, debris or stones. Tracking algorithm Envelope detector Shape analysis Track analysis Classification

  15. Testing a difficult case. Original raw- and SED-echogram Median 3x15 Treshold Region Contour Single echo filter -52 dB growing detection detection Drifting debris Two fishes Stones

  16. Conclusion:Combining image analysis with the traditional metod is promising! • The traditional SED is still reducing the fish detection probability. • Difficult to find one parameter setting that manage to handle all kinds of tracks and noise. More research is needed! • However we have shown that this method manages to: * Extract and use important information lost by the SED. * Reduce the creation of noise-based fish tracks. • The overall ability to detect fish in sonar data with low signal to noise ratio has been improved!

More Related