题目/Title：一种实时、自适应的侧扫声呐小目标分割算法
A real-time and adaptive algorithm for small target segmentation in side-scan sonar
作者/Author：李秋实, 王旭旸, 李国林, 张福海, 刘佳, 许枫, 谢翔
Qiushi Li, Changyu Wang, Guolin Li, Fuhai Zhang, Jia Liu, Xiang Xie
期刊/Journal：南开大学学报(自然科学版) Acta Scientiarum Naturalium Universitatis Nankaiensis
年份/Issue Date：2022.May
卷（期）及页码/Volume（No.）&pages：Vol.55, No.3, pp.1-6
摘要/Abstract：
针对侧扫成像声呐获取的水下小目标,提出了一种基于声呐原始回波数据的实时、自适应分割算法.该算法利用水底回波分布的统计特点,采用了一种基于威布尔分布模型的自适应阈值确定方法,实现对目标的初始分割;在此基础上提出了一种基于亮区回波特点的区域生长方法,得到精确的分割结果;之后利用霍夫检测方法合并过分割区域,利用小目标物理尺寸信息进行尺寸检测消除山脊、岩石等尺寸较大的虚假目标.实验结果表明,仅利用目标物理尺寸的检测方法,其速度能够达到3 s/帧,目标召回率达到100%,虚警率仅为34.62%.
A real-time and adaptive segmentation algorithm based on sonar raw echo data is proposed for small underwater targets acquired by side-scan imaging sonar. The algorithm uses the statistical characteristics of underwater echo distribution and adopts an adaptive threshold determination method based on Weibull distribution model to achieve the initial segmentation of targets; On this basis, a region growing method based on the characteristics of bright region echo is proposed to obtain accurate segmentation results; After that, Hough detection method is used to merge the segmented region, and small target physical size information is used for size detection to eliminate false targets with large size such as ridges and rocks. The experimental results show that the speed of the detection method using only target physical size can reach 3 s/frame, the target recall rate can reach 100%, and the false alarm rate is only 34.62%