基于GPA+CBAM的域自适应水下目标检测方法

刘麒东; 沈鑫; 刘海路; 丛璐; 付先平

doi:10.11993/j.issn.2096-3920.2023-0149

基于GPA+CBAM的域自适应水下目标检测方法

doi: 10.11993/j.issn.2096-3920.2023-0149

刘麒东^1,,
沈鑫^1,,
刘海路^1,,
丛璐^1,,
付先平^{1, 2, ,}

1.
大连海事大学信息科学技术学院 , 辽宁大连, 116026
2.
鹏城实验室, 广东深圳, 518000

基金项目: 国家自然科学基金项目(62002043、62176037); 辽宁省振兴人才计划项目(XLYC1908007); 辽宁省重点研发计划项目(201801728); 大连市科技创新基金项目(2021JJ12GX028、2019J11CY001、2018J12GX037).

详细信息

作者简介:
刘麒东(2000-), 男, 在读硕士, 主要研究方向为目标检测

通讯作者:
付先平(1971-), 男, 博士, 教授, 主要研究方向为计算机视觉和人工智能.

中图分类号: TJ630.34; U663
计量
- 文章访问数: 213
- HTML全文浏览量: 51
- PDF下载量: 92
- 被引次数: 0
出版历程
- 收稿日期: 2023-11-22
- 修回日期: 2024-01-01
- 录用日期: 2024-01-05
- 网络出版日期: 2024-02-01

Domain-Adaptive Underwater Target Detection Method Based on GPA + CBAM

LIU Qidong^1
,,
SHEN Xin^1
,,
LIU Hailu^1
,,
CONG Lu^1
,,
FU Xianping^{1, 2
, ,}

1.
The School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
2.
The Peng Cheng Laboratory, Shenzhen 518000, China

摘要

摘要: 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。
- 水下目标检测 /
- 图诱导原型对齐 /
- 域自适应 /
- 卷积块注意模块
Abstract: 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 detection /
- graph-induced prototype alignment /
- domain adaptation /
- convolutional block attention module

HTML全文

图 1 水下图像域偏移现象

Figure 1. Domain shift phenomenon in underwater images

下载: 全尺寸图片幻灯片

图 2 GPA检测框架

Figure 2. Testing framework of GPA

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图 3 SEM结构

Figure 3. Structure of SEM

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图 4 一般变分解码器与DRAW结构对比

Figure 4. Comparison between general variational decoder and DRAW structure

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图 5 CBAM结构

Figure 5. Structure of CBAM

下载: 全尺寸图片幻灯片

图 6 礁石地形注意力可视化

Figure 6. Attention visualization of reef terrain

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图 7 沙石地形注意力可视化

Figure 7. Attention visualization of sand and stone terrain

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图 8 实验检测结果

Figure 8. Experimental detection results

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表 1 水下数据集和公共数据集实验结果

Table 1. Experimental result of underwater dataset and public dataset

数据集	类别	精度/%
数据集	类别	基线	GPA	DA	文中方法
URPC2020→HMRD	海参	41.7	48.1	49.2	52.0
	海胆	62.3	67.9	68.1	69.7
	海星	47.4	54.7	58.6	60.1
	平均值	50.3	56.9	58.6	60.6
VOC12→VOC07	自行车	73.7	76.0	77.0	74.5
	鸟	69.3	68.1	75.0	68.6
	轿车	72.1	74.2	82.1	74.6
	牛	77.2	75.3	79.3	77.1
	狗	80.0	84.2	83.3	83.4
	人	76.2	77.8	76.0	77.6
	沙发	60.1	61.8	71.4	63.5
	火车	88.3	85.9	77.6	88.5
	平均值	74.6	75.4	77.7	76.0

下载: 导出CSV

表 2 公共数据集Sim10k→cityscapes实验结果

Table 2. Experimental result of public dataset Sim10k→cityscapes

方法	精度/%
基线	34.9
GPA	46.1
DA	45.8
文中方法	48.3

下载: 导出CSV

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