Abstract: With the in-depth development of underwater work in rivers,lakes and offshore near-shore shallow water areas,diver’s underwater engineering construction such as underwater salvage,positioning and exploration becomes significant.The TKIS-I helmet-mounted colorful imaging sonar developed by this lab has been acknowledged by Navigation and Warranty Department of Chinese Navy.Currently,there are more than two dozens of TKIS-I in service.However,under the complex underwater environment,divers usually perform underwater operations with great risks,so it is expected to use underwater robots to achieve automatic underwater target detection in the future.Aiming at the feature of sonar image,this paper adopted feature extraction methods of HSV color space,Histogram of Oriented Gradient(HOG) and Local Binary Pattern(LBP) respectively in the aspects of color,shape and texture.Besides,the paper improved multi-feature fusion method and used optimized support vector machine(SVM) for classification,aiming to quickly detect underwater targets to lay the foundation for robots’ underwater automatic target detection in the future.

Key words: Color image sonar, Histogram of oriented gradient(HOG), HSV color space, Local binary pattern(LBP), Multi-feature fusion, Support vector machine(SVM)