基于声呐图像的水下目标检测研究综述

Underwater Target Detection Based on Sonar Image

通过处理声呐图像实现水下目标检测具有重大的军事与民用意义。文章对基于声呐图像的水下目标检测原理、方法、算法和发展趋势进行了全面阐述。将基于声呐图像的水下目标检测任务分为传统水下目标检测、基于深度学习的目标检测,以及深度学习与迁移学习相结合的目标检测3个层面。又分别将传统目标检测分为基于数理统计、基于数学形态学以及基于像素的水下目标检测;将基于深度学习的目标检测分为基于一阶段方法、基于二阶段方法以及基于DETR的目标检测;将深度学习与迁移学习相结合的目标检测分为基于简单深度神经网络模型的迁移与基于复杂深度学习模型的迁移所实现的目标检测进行了具体讨论。最后总结归纳现有技术的优缺点,并对该领域的未来发展方向作出进一步的展望。

Underwater target detection by processing sonar images is of great military and civil significance.This papercomprehensively describes the principles,methods,algorithms,and development trends in underwater target detection basedon sonar images.Initially,we divide the underwater target detection task based on sonar images into traditional,deep learning-based,and combined deep learning-and transfer learning-based underwater target detection.Traditional target detection isdivided into underwater target detection based on mathematical statistics,mathematical morphology,and pixels.Deeplearning-based target detection methods are primarily divided into one-stage,two-stage,and detection transformer(DETR)methods.Combined deep learning-and transfer learning-based target detection is primarily divided into target detection basedon simple deep neural network model transfer and complex deep learning model transfer.Finally,the advantages anddisadvantages of the existing technology are summarized,and the future development direction of this field is discussed.