【摘要】 随着国内对河流、湖泊和海洋近岸浅水区域水下工作的深入开展,潜水员进行水下打捞、定位以及勘探等水下工程建设变得意义重大。本实验室开发的专利产品TKIS-I头盔式彩色图像声呐获得中国海军航行保障部认可,目前已有20多台服务于部队并持续获得部队订货。但是,在复杂的水下环境中,潜水员进行水下作业具有较大的风险,所以期望今后能利用水下机器人实现自动水下目标检测,从而把潜水员从危险的复杂水下活动中解放出来。为此,文中针对声呐图像的特点,在颜色、形状、纹理3个方面分别采取了HSV颜色空间、梯度直方图(HOG)、局部二值模式(LBP)的特征提取方法,并且改进了多特征融合的方式,使用优化后的支持向量机(SVM)进行分类,旨在快速检测出水下目标,为以后水下机器人的自动目标检测奠定基础。更多还原

【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.更多还原