摘要
轻量化目标检测模型SSD-MV3(Single Shot Detection-MobileNet V3)因输入图像尺寸限制无法直接检测高分辨率大尺寸合成孔径声纳(Synthetic Aperture Sonar,SAS)图像中感兴趣小目标.为此,本文提出了一种新的目标检测方法HRSSD(High Resolution Single Shot Detection),该方法通过冗余切割确保SSD-MV3输入图像尺寸的规范以及感兴趣小目标的完整,并利用二次非极大值抑制保证检测结果的唯一.此外,提出了一种尺度、空间和通道注意力机制联合的特征提取模块,并利用该模块重新设计了SSD-MV3的基础网络和附加特征提取网络,记作SSD-MV3P(Single Shot Detection-MobileNet V3 Pro),使得SSD-MV3P能更有效的感知感兴趣小目标特征信息.实验结果表明,在感兴趣小目标检测数据集SST(Sonar Small Targets)上,SSD-MV3P的平均检测精度(mean Average Precision,mAP)比SSD-MV3提升4.39%.HRSSD实现了高分辨率大尺寸SAS图像感兴趣小目标的检测,并且保证了同一位置上检测结果的完整性和唯一性.
The efficient object detection model SSD-MV3(Single Shot Detection-MobileNet V3)cannot directly de⁃tect the interested small targets in high-resolution SAS(Synthetic Aperture Sonar)images due to the input image size limit.To this end,this paper proposes a novel object detection method,HRSSD(High Resolution Single Shot Detection),which ensures the specification of SSD-MV3 input image size and the integrity of the interested small targets through redundant cutting algorithm,and guarantees the unique detection result by using secondary non-maximum suppression.Furthermore,an improved feature block with a combination of scale,space and channel attention mechanism is proposed,and the basic network and additional feature network of SSD-MV3 are redesigned as SSD-MV3P(Single Shot Detection-MobileNet V3 Pro).Thus,SSD-MV3P can more effectively perceive the feature information of interested small targets.The experimental results show that the mAP(mean Average Precision)of SSD-MV3P is 4.39%higher than that of SSD-MV3 on the interest⁃ed small target detection dataset SST(Sonar Small Target).HRSSD realizes the detection of the interested small targets in high-resolution SAS images,and ensures the integrity and uniqueness of the detection result at the same location.
作者
李宝奇
黄海宁
刘纪元
刘正君
韦琳哲
LI Bao-qi;HUANG Hai-ning;LIU Ji-yuan;LIU Zheng-jun;WEI Lin-zhe(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;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2024年第3期762-771,共10页
Acta Electronica Sinica
基金
国家自然科学基金(No.11904386)。
关键词
合成孔径声纳
感兴趣小目标检测
轻量化目标检测模型
注意力机制
二次非极大值抑制
synthetic aperture sonar
interested small target detection
efficient object detection model
attention mechanism
secondary non maximum suppression
