首页|基于CSLS-CycleGAN的侧扫声纳水下目标图像样本扩增法

基于CSLS-CycleGAN的侧扫声纳水下目标图像样本扩增法

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针对侧扫声纳水下目标图像稀缺,获取难度大、成本高,导致基于深度学习的目标检测模型性能差的问题,结合光学域类目标数据集丰富的现状,提出一种基于通道和空间注意力(channel and spatial attention,CSA)模块、最小二乘生成对抗生成网络(least squares generative adversarial networks,LSGAN)及循环对抗生成网络(cycle generative adversarial networks,CycleGAN)的侧扫声纳水下目标图像样本扩增方法.首先,受CycleGAN的启发,设计基于循环一致性的单循环网络结构,保证模型的训练效率.然后,在生成器中融合CSA模块,减少信息弥散的同时增强跨纬度交互.最后,设计了基于LSGAN的损失函数,提高生成图像质量的同时提高训练稳定性.在船舶光学域数据集与侧扫声纳沉船数据集上进行实验,所提方法实现了光学—侧扫声纳样本间信息的高效、稳健转换以及大量侧扫声纳目标样本的扩增.同时,基于本文生成样本训练后的检测模型进行了水下目标检测,结果表明,使用本文样本扩增数据训练后的模型在少样本沉船目标检测的平均准确率达到了 84.71％,证明了所提方法实现了零样本和小样本水下强代表性目标样本的高质量扩增,并为高性能水下目标检测模型构建提供了一种新的途径.
CSLS-CycleGAN based side-scan sonar sample augmentation method for underwater target image
In view of the scarcity,difficulty and high cost of side-scan sonar underwater target images,and the poor performance of deep-learning based target detection model,combined with the abundant target data set in optical domain,a sample augmentation method for underwater target side-scan sonar images based on channel and spatial attention(CSA)module and least squares generative adversarial networks(LSGAN)and cycle generative adversarial networks(CycleGAN)is propesed.Firstly,inspired by CycleGAN,a single cycle network structure based on cycle consistency is designed to ensure the training efficiency of the model.Then,the CSA module is integrated into the generator to reduce information dispersion while enhancing cross-latitude interaction.Finally,a loss function based on LSGAN is designed to improve the quality of the generated image while improving the training stability.Experiments are carried out on ship optical domain data set and side-scan sonar shipwreck data set.The results show that the proposed method achieves efficient and robust conversion of information between optical and side-scan sonar samples and augmentation of a large number of side-scan sonar target samples.At the same time,the underwater target detection is carried out based on the detection model generated after sample training in this paper.The results show that the average precision value of the model after training with sample augmentation data in this paper reachs 84.71％in detecting shipwreck targets with few samples,which proves that the method in this paper achieves high-quality amplification of highly representative underwater target samples with zero samples and small samples.It also provides a new way to construct high-performance underwater target detection model.

sample augmentationside-scan sonarcycle generative adversarial networks(CycleGAN)channel and spatial attention(CSA)moduleleast squares generative adversarial networks(LSGAN)

汤寓麟、王黎明、余德荧、李厚朴、刘敏、张卫东

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海军工程大学电气工程学院,湖北武汉 430033

中国人民解放军91001部队,北京 100841

中国人民解放军31016部队,北京 100088

样本扩增 侧扫声纳 循环生成对抗网络 通道和空间注意力模块 最小二乘生成对抗网络

国家优秀青年科学基金国家自然科学基金国家自然科学基金国家自然科学基金湖北省杰出青年科学基金

421220254197400541971416420740742019CFA086

2024

10.12305/j.issn.1001-506X.2024.05.06

系统工程与电子技术
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会

系统工程与电子技术

CSTPCD北大核心
影响因子：0.847
ISSN：1001-506X
年,卷(期)：2024.46(5)
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