基于改进SSD的水下光学图像感兴趣目标检测算法研究  

Underwater Optical Image Interested Object Detection Model Based on Improved SSD

作　　者：李宝奇 黄海宁[1,2] 刘纪元[1,2] 刘正君 韦琳哲 LI Baoqi;HUANG Haining;LIU Jiyuan;LIU Zhengjun;WEI Linzhe(Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China;Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院声学研究所,北京100190 [2]中国科学院先进水下信息技术重点实验室,北京100190

出　　处：《电子与信息学报》2022年第10期3372-3378,共7页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(11904386);国家基础科研计划重大项目(JCKY2016206A003);中国科学院青年创新促进会(2019023)。

摘　　要：针对轻量化目标模型SSD-MV2对水下光学图像感兴趣目标检测精度低的问题,该文提出一种通道可选择的轻量化特征提取模块(SEB)和一种卷积核可变形、通道可选择的特征提取模块(SDB)。与此同时,利用SEB模块和SDB模块分别重新设计了SSD-MV2的基础网络和附加特征提取网络,记作SSD-MV2SDB,并为其选择了合理的基础网络扩张系数和附加特征提取网络SDB模块数量。在水下图像感兴趣目标检测数据集UOI-DET上,SSD-MV2SDB比SSD-MV2检测精度提高3.04%。实验结果表明,SSD-MV2SDB适用于水下图像感兴趣目标检测任务。In order to solve the problem of low detection accuracy of SSD-MV2,a Selective and Efficient Block(SEB)and a Selective and Deformable Block(SDB)are proposed.At the same time,the basic network and additional feature extraction network of SSD-MV2 are redesigned by using SEB and SDB,which is named SSDMV2SDB,and a set of reasonable expansion coefficient of basic network and number of SDB in additional feature extraction network are selected for SSD-MV2SDB.On UOI-DET,mAP of SSD-MV2SDB is 3.04%higher than that of SSD-MV2.The experimental results show that SSD-MV2SDB is suitable for underwater optical image interested object detection task.

关 键 词：水下光学图像感兴趣目标检测 SSD MobileNet V2 可变形卷积 通道可选择 

分 类 号：TN911.73[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]