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基于改进SSD的合成孔径声纳图像感兴趣小目标检测方法

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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 Detec-tion-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.
Interested Small Target Detection Method Based on Improved SSD for Synthetic Aperture Sonar Image
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.

synthetic aperture sonarinterested small target detectionefficient object detection modelattention mechanismsecondary non maximum suppression

李宝奇、黄海宁、刘纪元、刘正君、韦琳哲

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中国科学院声学研究所,北京 100190

中国科学院先进水下信息技术重点实验室,北京 100190

中国科学院大学,北京 100049

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

国家自然科学基金

11904386

2024

电子学报
中国电子学会

电子学报

CSTPCD北大核心
影响因子:1.237
ISSN:0372-2112
年,卷(期):2024.52(3)
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