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Automated Detection and Classification Models A Model-Based Approach to the Detection and Classification of Mines in Side-scan Sonar S.Reed,

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Presentation on theme: "Automated Detection and Classification Models A Model-Based Approach to the Detection and Classification of Mines in Side-scan Sonar S.Reed,"— Presentation transcript:

1 Automated Detection and Classification Models A Model-Based Approach to the Detection and Classification of Mines in Side-scan Sonar S.Reed, Y.Petillot, J.Bell

2 Automated Detection and Classification Models Contents Why Use Unsupervised Techniques? Our Proposed CAD/CAC algorithm. The Sonar Process. Automated Object Detection. Extraction of Object Features. Automated Object Classification. Future Research. Conclusions.

3 Automated Detection and Classification Models Unsupervised Techniques Rapid Advances in AUV Technology. On-board analysis now required. Large amounts of data quickly available for analysis.

4 Automated Detection and Classification Models Unsupervised Techniques Future automated systems will require all available information (navigation data, image processing models.etc.) to be fused.

5 Automated Detection and Classification Models CAD/CAC Proposal Detect MLO’s (MRF-based Model) Fuse Other Views Extract Highlight/Shadow (CSS Model) Classify Object (Dempster-Shafer) False Alarm? Positive Classification? 12YES NO MINE REMOVE FALSE ALARM

6 Automated Detection and Classification Models The Sonar Process Sonar images represent the time of flight of the sound rather than distance. Objects appear as a highlight/shadow pair in the sonar image.

7 Automated Detection and Classification Models The Detection Model A Markov Random Field(MRF) model framework is used. MRF models operate well on noisy images. A priori information can be easily incorporated. They are used to retrieve the underlying label field (e.g shadow/non-shadow)

8 Automated Detection and Classification Models Basic MRF Theory A pixel’s class is determined by 2 terms: –The probability of being drawn from each classes distribution. –The classes of its neighbouring pixels.

9 Automated Detection and Classification Models Incorporating A Priori Info Object-highlight regions appear as small, dense clusters. Most highlight regions have an accompanying shadow region. Segment by minimising:

10 Automated Detection and Classification Models Initial Detection Results Initial Results Good. Model sometimes detects false alarms due to clutter such as the surface return – requires more analysis! DETECTED OBJECT

11 Automated Detection and Classification Models Object Feature Extraction The object’s shadow is often extracted for classification. The shadow region is generally more reliable than the object’s highlight region for classification. Most shadow extraction models operate well on flat seafloors but give poor results on complex seafloors.

12 Automated Detection and Classification Models The CSS Model 2 Statistical Snakes segment the mugshot image into 3 regions : object-highlight, object-shadow and background. A priori information is modelled: The highlight is brighter than the shadow An object’s shadow region can only be as wide as its highlight region.

13 Automated Detection and Classification Models CSS Results CSS Model Standard Model

14 Automated Detection and Classification Models The Combined Model Objects detected by MRF model are put through the CSS model. The CSS snakes are initialised using the label field from the detection result. This ensures a confident initialisation each time. The CSS can detect MANY of the false alarms. False alarms without 3 distinct regions ensure the snakes rapidly expand, identifying the detection as a false alarm. Navigation info is also used to produce height information which can also remove false alarms.

15 Automated Detection and Classification Models Results

16 Automated Detection and Classification Models Results 2

17 Automated Detection and Classification Models Results 3

18 Automated Detection and Classification Models Result 4

19 Automated Detection and Classification Models BP ’02 Results The combined detection/CSS model was run on 200 BP’02 data files containing 70 objects. 80% of the objects where detected and features extracted(for classification) false alarms per image. The surface return resulted in some of the objects not being detected. Dealing with this would produce a detection rate of ~ 91%.

20 Automated Detection and Classification Models Object Classification The extracted object’s shadow can be used for classification. We extend the classic mine/not-mine classification to provide shape and dimension information. The non-linear nature of the shadow-forming process ensures finding relevant invariant features is difficult. Shadows from the same object

21 Automated Detection and Classification Models Modelling the Sonar Process Mines can be approximated as simple shapes – cylinders, spheres and truncated cones. Using Nav data to slant-range correct, we can generate synthetic shadows under the same sonar conditions as the object was detected. Simple line-of-sight sonar simulator. Very fast.

22 Automated Detection and Classification Models Comparing the Shadows Iterative Technique is required to find best fit. Parameter space limited by considering highlight and shadow length. Synthetic and real shadow compared using the Hausdorff Distance. It measures the mismatch of the 2 shapes. HAUSDORFF DISTANCE

23 Automated Detection and Classification Models Incorporating Knowledge As the technique is model-based, information on likely mine dimensions can be incorporated. Limited information from the highlight region can also be used to distinguish between the tested classes. We obtain an overall membership function for each class.

24 Automated Detection and Classification Models The Classification Decision A decision could be made by simply defining a ‘Positive Classification Threshold’. This is a ‘hard’ decision and non-changeable. The ‘lawnmower’ nature of Sidescan surveys ensures the same object is often viewed multiple times. The model should ideally be capable of multi-view classification. We use DEMPSTER-SHAFER theory.

25 Automated Detection and Classification Models Mono-view Results Dempster-Shafer allocates a BELIEF to each class. Unlike Bayesian or Fuzzy methods, D-S theory can also consider union of classes. Bel(cyl)=0.83 Bel(sph)=0.0 Bel(cone)=0.0 Bel(clutter)=0.08 Bel(cyl)=0.0 Bel(sph)=0.303 Bel(cone)=0.45 Bel(clutter)=0.045 Bel(cyl)=0.42 Bel(sph)=0.0 Bel(cone)=0.0 Bel(clutter)=0.46

26 Automated Detection and Classification Models Mono-view Results Model was tested on 66 mugshots containing cylinders, Spheres, Truncated cones and clutter objects.

27 Automated Detection and Classification Models Multi-view Analysis Dempster-Shafer allows results from multiple views to be fused. Mono-Image BeliefFused Belief ObjCylSphConeCluttObjs Fused CylSphConeClutt , ,2, ,2,3,

28 Automated Detection and Classification Models Multi-Image Analysis Mono-Image BeliefFused Belief ObjCylSphConeCluttObjs Fused CylSphConeClutt , ,6, ,6,7,

29 Automated Detection and Classification Models Future Research The current detection model considers objects as a Highlight/Shadow pair. An object can also be considered as a discrepancy in the surrounding texture field.

30 Automated Detection and Classification Models Conclusions Automated Detection/Feature Extraction model has been developed and tested on a large amount of data. Good Results obtained, improvements expected when surface returns removed. Classification model uses a simple sonar simulator and Dempster-Shafer theory to classify the objects. Extends mine/not-mine classification to provide shape and size information. Future research is focusing on texture segmentation to complement the current work.

31 Automated Detection and Classification Models Acknowledgements We would like to thank the following institutions for their support and for providing data: DRDC–Atlantic, Canada Saclant Centre, Italy GESMA, France


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