Presentation on theme: "1 Improved fish detection probability in data from split- beam sonars. Helge Balk and Torfinn Lindem. Department of Physics. University of Oslo."— 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. u Development of sonar software. u Most data collected with Simrad EY500. u Some data collected with HTI modell-243 u Experience from fieldwork. u River Tornio (Finland summer 97) u Lake Semsvannet. (Norway winter 98, 99) u River Tana (Norway summer 98, 99) u (Data from various other rivers and lakes.)
4 Horizontal application in shallow rivers: The traditional method tends to faile because of: u Increased noise-level. (Rain, silt, running water, bottom and surface reflections, air-bubles, debris ) u Increased phase and amplitude fluctuations in the echo-signal. ( side-aspect, reflection, transducer-vibrations, moving sound-media ) u Multiple object problem. (fish, debris, stones....;- Classification nessesary)
5 SED;- the main problem. u Noise is too easily detected as single targets. u 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 Classification Tracking SED SED-Echogram
6 Tracking algorithm. a) Missing echoes results in rejection of fish-tracks. b) Noise-echoes results in creations of artificial fish-like tracks. Four salmons a rainy day in Tana. Tracking result SED-echogram Classification Tracking SED
7 How to improve the method. Four salmons on a rainy day in Tana. Raw-echogram SED-echogram Classification Tracking SED Collect more data. Extract more information from existing data.
10 Convolution and window operations. Echogram array F(m 1, m 2 ) I H G F E D C B A I H G F E D C B A F (n 1, n 2 ) = Input image array H(m 1, m 2 ) = Impulse response array Q(m1,m2) = Output image array Window producing one output pixel Hit-miss, mean, roberts-c, laplace
11Filtering. u Many well-known filters availabel. u Low-pass: Median, Mean, Knn, Sigma, u High-Pass: Sobel, Robert’s, Prewitt, Gradient, Laplace. u Morphologic filters: Hit-miss, Hit-add. u Not always an improvement. u 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. u Edgebased. u Detecting edges. (high-pass: gradient, Laplace.) u Linking. u Region based. u Tresholding. u Growing and shrinking. u Seeds. u Split and merge. u Relaxation. Separating background and foreground.
13 Shape analysis. u Central moments. u Radius of gyration. u Orientation. u Topological features. u Area. u Contour length and smoothness. u Compactness. u Eccentricity. u Small shapes originates from surface noise and airbubbles. u High fluctuation in contour and large area may indicate bottom structures. u Fish and debris seen to produce thin and smooth tracks.
14 Putting things together. Single Echo Detector Track analysis Classification Raw- echogram Image processor Regions of fish, debris or stones. Contour detector Shape analysis Tracking algorithm Phase detector Envelope detector
15 Median 3x15 Treshold Region Contour Single echo filter -52 dB growing detection detection Two fishes Stones Drifting debris Testing a difficult case. Original raw- and SED-echogram
16 Conclusion: Conclusion: Combining image analysis with the traditional metod is promising! u The traditional SED is still reducing the fish detection probability. u Difficult to find one parameter setting that manage to handle all kinds of tracks and noise. More research is needed! u 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. u The overall ability to detect fish in sonar data with low signal to noise ratio has been improved!