Research Of Ship Detection Algorithms For Intelligent Video Maritime Monitoring

China possesses a long coastline, so it is necessary to develop automatic monitoring andsurveillance system for its maritime space. In military area, it is of great significance forsafeguarding the maritime rights and interests, strengthening supervision and management ofimportant sea areas, and reducing the maritime disputes and so on. In civil area, it has manyapplications such as marine traffic management in port or bay, ocean environment monitoring,protection of exclusive economical zones, searching and rescuing of ships from wrecks, andtaking measures against illegal activities such as illegal fishing, smuggling, and immigration.Currently, the major impediment to intelligent video maritime monitoring is how to fast androbustlydetecttheseasurfacetargetsundervariousseastatesforbuoy-basedorship-basedvisualsurveillance, which isoneofthekeytechnologies for intelligent maritime visualmonitoring. Toovercometheabove-mentionedobstacle,thisthesisinvestigatesthefastshipdetectionalgorithmsandrestorationalgorithmofseafogdegradedimagesfordynamicmaritimevisualsurveillanceindepth,andhasaccomplishedthefollowingresearchwork.(1) A fast algorithm to detect sea surface ship targets based on the visual selective attentionmechanism in wavelet domain is proposed in the dissertation. According to the characteristics ofhumanvisualobservation,that is,thecontoursofobjectsishighlightedat largescaleandthedetailsof object is focused at small scale, a two-scale visual attention model is first established in thewaveletdomainbyusing liftingwavelettransform.Thentwo visualsaliencemapsaregeneratedbyemploying the phase spectrum method and gradient based method on the low-pass subband ofwavelet domain withcoarseresolutionrespectively, and asynthetic visualsalience map isobtainedby effective combination of the both obtained salient maps. Finally, the high resolution visualsaliencymapoforiginalimageisgeneratedbyinversewavelettransform,andtheseasurfaceobjectregionsareextracted fromthe final saliency map. Theexperimentalresults showthat theproposedalgorithm can detect the maritime targets quickly and accurately, so it can be used for maritimebuoy-basedintelligentmonitoring.(2) A novel algorithm for ship target detection based on texture model of sea surface ispresented inthedissertation. Accordingtothe factthatthetexture featureofseasurface isbasicallyuniform and consistent, a segmentation strategy from simple to complex is introduced in theproposed algorithm. Firstly, the simple sky background and horizon are obtained quickly using anenergyfeatureinDCTdomainofimageblocks.Secondly, inordertoseparateshiptargetsfromthecomplex sea background belowthe horizon, a newtexture Gaussian mixture modelofsea surfacebased on imageblocks inDCTdomain isdeveloped,then fromtheconstructed seasurface model,ship targets are segmented fromsea background. The experimentalresults showthat the proposedalgorithm can detect ship targets robustly and satisfy the real-time requirement of visual maritimesurveillance from non-stationary platforms, especially suitable for sea background with high sea state.(3) A visibility improving algorithm for sea fog images based on contourlet transform isdeveloped. Firstly, the RGB color space is transformed to the YUV color space. Then, for theluminance component Y, from physical formation model of fog images, the low frequencycomponent and the high frequency component is separated effectively from contourlet transform.Secondly,theamountofmediascattering light isestimatedandremovedbyusingadaptivebilateralfiltering on the low-pass subband of contourlet transform, thereby simultaneously eliminating thehalo effect;and forthe high-passsubbandsofcontourlet transform,thenoise is filtered byadaptivethresholding and thetexturedetails areenhanced adaptively. Thirdly, thereconstructed Yimage byinverse contourlet transform is further enhanced through histogram equalization to improve theglobalcontrast.Finally,thevisibilityenhancedimageisobtainedbycombinationtheresultedimageofYcomponentandthechrominancecomponentsofUandV. Theexperimentalresultsshowthatthe proposed algorithm can significantly reduce the processing time and effectively improve thevisibilityoffogdegradedimages.