首页|改进PP-YOLOv2的水下侧扫声呐图像多目标识别

改进PP-YOLOv2的水下侧扫声呐图像多目标识别

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针对水下侧扫声呐图像对比度低、噪声强度大,现有深度学习方法特征提取能力不足的问题,提出基于PP-YOLOv2引入注意力机制的改进侧扫声呐多目标识别方法.首先,针对侧扫声呐图像信噪比大、不同声呐设备生成的图像大小不一等特点,设计有效的图像预处理方法,包括噪声过滤、图像增强等;其次,基于当前目标检测性能很好的PP-YOLOv2模型设计改进,更换BotNet-dcn为模型主干网络,引入注意力机制提高网络特征提能力;最后,设计新的解耦头替换原耦合检测头,针对侧扫声呐图像的小目标进行精细化预测.结果表明:与原始PP-YOLOv2相比,所提方法在平均识别精度上提升了 4.4％;与两种主流的基于卷积神经网络的方法相比,所提方法在平均识别精度上分别提升了 4.66％和5.42％,同时在识别效率上分别提升32.4％和27.6％.
Multi-object Recognition of Underwater Side-scan Sonar Image Based on Improved PP-YOLOv2
Because of the low contrast and high noise intensity of underwater side-scan sonar images,the feature extraction ability of existing deep learning methods is still insufficient.An improved side-scan sonar multi-target recognition method is proposed based on PP-YOLOv2 by introducing attention mecha-nism.First,for the characteristics of side-scan sonar images with high signal-to-noise ratio and different image sizes generated by different sonar devices,several effective image preprocessing method are explored,including noise filtering,image data augmentation,etc.Secondly,based on PP-YOLOv2,which is a state-of-the-art target detection method with good performance both in terms of precision and efficiency,a new model is designed by replacing the backbone network with BotNet-DCN.By doing this,the atten-tion mechanism is introduced to improve the network feature improvement ability.Finally,a new decoupled head is designed to replace the original coupled head to perform refined prediction for small targets in side-scan sonar images.The results show that compared with the original PP-YOLOv2,the proposed method improves the average recognition accuracy by 4.4％;compared with the two mainstream methods based on convolutional neural network,the proposed method improves the average recognition accuracy by 4.66％and 5.42％,respectively,and improves the recognition efficiency by 32.4％and 27.6％,respectively.

underwater side-scan sonarmulti-object recognitionPP-YOLOv2attention mechanismdecoupled head

王芳、李慧涛、王凯、魏薇、李晶、张立立

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北京石油化工学院信息工程学院,北京 102617

军事科学院国防科技创新研究所,北京 100036

戌峰科技有限公司,宁夏银川 750011

水下侧扫声呐 多目标识别 PP-YOLOv2 图像预处理 注意力机制 解耦头

北京市教委科技计划一般项目北京市教委科技计划一般项目北京市科学技术协会2021-2023年度青年人才托举工程项目宁夏自然科学基金北京石油化工学院交叉科研探索项目北京石油化工学院校级教育教学改革与研究重点项目北京石油化工学院校级教育教学改革与研究重点项目北京石油化工学院校级教育教学改革与研究重点项目2023年国家级大学生创新创业计划项目

KM201910017006KM202010017011KXTJ01642022AAC03757BIPTCSF-006ZDKCSZ202103002ZDFSGG202103001ZD2021030012023J00212

2024

南开大学学报(自然科学版)
南开大学

南开大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.284
ISSN：0465-7942
年,卷(期)：2024.57(3)
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