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基于GPA+CBAM的域自适应水下目标检测方法

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  • 维普
针对水下目标检测易出现域偏移而导致检测精度下降的现象,文中提出了基于图诱导原型对齐(GPA)的域自适应水下目标检测方法.该方法通过区域建议之间基于图的信息传播得到图像中的实例级特征,导出每个类别的原型表示用于类别级域对齐,从而聚合水下目标的不同模态信息,以此实现源域和目标域的对齐,减少域偏移带来的影响;同时添加了卷积块注意模块(CBAM),使神经网络能够专注于不同水域分布下的实例级特征.实验结果证明该方法能够有效提高发生域偏移时的检测精度.
Domain-Adaptive Underwater Target Detection Method Based on GPA+CBAM
Underwater target detection is often more susceptible to domain shift and reduced detection accuracy.In response to this phenomenon,this article proposed a domain-adaptive underwater target detection method based on graph-induced prototype alignment(GPA).GPA obtained instance-level features in the image through graph-based information propagation between region proposals and then derived prototype representations for category-level domain alignment.The above operations could effectively aggregate different modal information of underwater targets,thereby achieving alignment between the source and target domains and reducing the impact of domain shift.In addition,in order to make the neural network focus on instance-level features under different water domain distributions,a convolutional block attention module(CBAM)was added.The experimental results have shown that the proposed method can effectively improve detection accuracy during domain shift.

underwater target detectiongraph-induced prototype alignmentdomain adaptationconvolutional block attention module

刘麒东、沈鑫、刘海路、丛璐、付先平

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大连海事大学 信息科学技术学院,辽宁 大连,116026

鹏城实验室,广东 深圳,518000

水下目标检测 图诱导原型对齐 域自适应 卷积块注意模块

国家自然科学基金项目国家自然科学基金项目辽宁省振兴人才计划项目辽宁省重点研发计划项目大连市科技创新基金项目大连市科技创新基金项目大连市科技创新基金项目

6200204362176037XLYC19080072018017282021JJ12GX0282019J11CY0012018J12GX037

2024

10.11993/j.issn.2096-3920.2023-0149

水下无人系统学报
中国船舶重工集团公司第七〇五研究所

水下无人系统学报

CSTPCD
影响因子：0.251
ISSN：2096-3920
年,卷(期)：2024.32(5)