融合重参数化与注意力机制的水下视觉多目标跟踪算法

李军毅; 何铭乐; 刘畅; 徐雍

doi:10.11993/j.issn.2096-3920.2025-0012

融合重参数化与注意力机制的水下视觉多目标跟踪算法

doi: 10.11993/j.issn.2096-3920.2025-0012

李军毅^{1, 2, 3},
何铭乐^{1, 2, 3},
刘畅^{1, 2, 3},
徐雍^{1, 2, 3}

1.
广东工业大学自动化学院, 广东广州, 510006
2.
广东工业大学粤港智能决策与协同控制联合实验室, 广东广州, 510006
3.
广东工业大学广东省智能决策与协同控制重点实验室, 广东广州, 510006

基金项目: 国家自然科学基金项目资助(62206063、62121004、U22A2044); 广东省基础与应用基础研究基金项目(2024A1515010369).

详细信息

作者简介:
李军毅(1991-), 男, 博士, 助理研究员, 主要研究方向为水下无人自主系统控制理论与应用

中图分类号: TJ630.34; U674.76
计量
- 文章访问数: 13
- HTML全文浏览量: 11
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2025-01-15
- 修回日期: 2025-02-20
- 录用日期: 2025-02-25
- 网络出版日期: 2025-03-10

Underwater Visual Multiple Object Tracking Algorithm Integrating Re-parameterization and Attention Mechanism

LI Junyi^{1, 2, 3},
HE Mingle^{1, 2, 3},
LIU Chang^{1, 2, 3},
XU Yong^{1, 2, 3}

1.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
2.
Guangdong-Hong Kong Joint La-boratory for Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China
3.
Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China

摘要

摘要: 复杂的水下环境会严重影响成像设备的稳定性和获取图像的质量, 从而给水下无人自主系统视觉多目标跟踪带来极大挑战。为了解决水下相机抖动和图像退化带来的困难, 文中提出一种适用于水下无人自主系统的融合重参数化与注意力机制的水下视觉多目标跟踪算法。首先, 针对水下目标多样及图像退化等问题, 提出基于重参数化和注意力机制改进的YOLOv8 算法(RA-YOLOv8), 通过融合结构重参数化的多尺度特征提取卷积结构(DBB-RFAConv)和注意力机制, 有效增强网络的多尺度特征提取能力和提高模型的检测精度; 然后, 针对水下相机抖动问题给目标实时跟踪带来的挑战, 提出基于Inner-PIoUv2 改进的ByteTrack算法(IP2-ByteTrack), 使用 Inner-PIoUv2 作为跟踪算法匹配过程中的相似度度量, 增强模型在水下检测和跟踪任务中的性能, 提高跟踪轨迹匹配准确性; 最后, 基于RA-YOLOv8 和IP2-ByteTrack 算法, 提出一种用于水下无人自主系统的融合重参数化与注意力机制的水下视觉多目标跟踪算法。实验结果表明, 所提算法在复杂水下环境中表现出优异的性能, 能够有效解决现有方法在水下多目标跟踪中的不足。
- 水下多目标跟踪 /
- YOLO /
- ByteTrack /
- 重参数化 /
- 注意力机制
Abstract: The complex underwater environment can severely impact the stability of imaging devices and the quality of captured images, posing significant challenges for visual multi-object tracking in underwater unmanned autonomous systems. To address the difficulties arising from underwater camera jitter and image degradation, this paper proposes an underwater visual multi-object tracking algorithm that integrates re-parameterization and attention mechanisms, specifically tailored for underwater unmanned autonomous systems. First, to tackle the diversity of underwater targets and image degradation in target recognition, a reparameterization and attention-enhanced YOLOv8 algorithm (RA-YOLOv8) is proposed. This algorithm effectively enhances the network’s multi-scale feature extraction capability and improves detection accuracy by integrating a structurally reparameterized multi-scale feature extraction convolutional structure (DBB-RFAConv) and the AMSCE-attention mechanism; Then, to address the challenges of target real-time tracking caused by underwater camera shake, an Inner-PIoUv2-enhanced ByteTrack algorithm (IP2-ByteTrack) is proposed. Inner-PIoUv2 is used as the similarity measure in the matching process of the tracking algorithm, which enhances the model’s performance in underwater detection and tracking tasks, improving the accuracy of tracking trajectory matching; Finally, based on the RA-YOLOv8 and IP2-ByteTrack algorithms, a fusion reparameterization and attention mechanism-based underwater vision multiple object tracking algorithm for underwater autonomous systems is proposed. Experimental results show that the proposed algorithm exhibits excellent performance in complex underwater environments and can effectively address the shortcomings of existing methods in underwater multiple object tracking.
- UMOT /
- YOLO /
- ByteTrack /
- Re-parameterization /
- Attention Mechanism

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图 1 DBB-RFAConv的网络结构图

Figure 1. Network architecture diagram of DBB-RFAConv

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图 2 AMSCE-Attention的网络结构图

Figure 2. Network architecture diagram of AMSCE-Attention

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图 3 改进后的YOLOv8的网络结构图

Figure 3. Network structure diagram of improved yolov8

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图 4 Inner-PIoUv2的结构图

Figure 4. The structure diagram of Inner-PIoUv2

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图 5 水下多目标跟踪算法完整流程图

Figure 5. Complete flow chart of underwater multi-target tracking algorithm

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图 6 训练阶段和验证阶段中的损失函数和评估指标图

Figure 6. Chart of loss functions and evaluation metrics during training and validation phases

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图 7 可视化检测结果对比图

Figure 7. Comparison chart of visual test results

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图 8 可视化跟踪结果对比图

Figure 8. Comparison chart of visual tracking results

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表 1 实验环境配置

Table 1. The configuration of the experimental environment

名称	版本
CPU	Intel 13th Gen i9-13900HX
GPU	NVIDIA GeForce RTX 4060
内存	64G
操作系统	Windows11
Python	3.11

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表 2 不同YOLO系列模型性能对比实验结果

Table 2. Experimental results of performance comparison of different Yolo series models

模型	精确度	召回率	mAP50	mAP50-95	FPS
YOLOv7	81.5%	72.0%	75.0%	31.6%	35.46
YOLOv8	83.2%	73.5%	82.1%	36.6%	35.58
YOLOv10	72.0%	71.7%	77.5%	36.4%	35.84

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表 3 DBB-RFAConv的消融试验实验结果

Table 3. The experimental results of ablation studies on DBB-RFAConv

模型	精确度	召回率	mAP50	mAP50-95	GFLOPs	参数量
YOLOv8 (Baseline)	83.2%	73.5%	82.1%	36.6%	8.1	3 005 843
YOLOv8 + RFAConv	84.9%	72.9%	82.2%	37.4%	8.2	3 016 211
YOLOv8 + DBB- RFAConv	84.3%	75.8%	85.4%	37.6%	8.2	3 017 075

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表 4 不同注意力的消融实验结果

Table 4. The experimental results of ablation studies on different types of attention

模型	精确度	召回率	mAP50	mAP50-95	GFLOPs
YOLOv8 + DBB- RFAConv(Baseline)	84.3%	75.8%	85.4%	37.6%	8.2
Baseline + SimAM	82.7%	72.3%	82.7%	36.6%	8.2
Baseline + CBAM	82.9%	75.7%	83.6%	37.5%	8.2
Baseline + DHSA	87.8%	72.4%	83.2%	37.6%	8.5
Baseline + EMA	87.9%	79.1%	85.9%	37.8%	8.2
Baseline + MAB	83.1%	74.9%	82.3%	37.4%	8.6
Baseline + MSCA	87.0%	75.2%	83.8%	37.9%	8.4
Baseline + CPCA	86.4%	77.3%	83.5%	38.2%	8.3
Baseline + AMSCE- Attention(Ours)	89.4%	77.0%	86.2%	38.8%	8.2

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表 5 不同多目标跟踪算法的实验结果

Table 5. Experimental results of different multi-object tracking algorithms

模型	IDF1	IDP	IDR	IDs	MOTA	MOTP	FPS
YOLOv8 + ByteTrack	65.5%	86.2%	52.8%	21.4	54%	0.387	24.92
YOLOv8 + BoT-SORT	69.68%	90.64%	56.59%	14.5	60.6%	0.431	10.02
YOLOv8 + OC-SORT	60.6%	85.3%	47.2%	22.2	49.2%	0.342	24.11
YOLOv8 + DeepOC-SORT	60.6%	85.5%	47.1%	21.4	49.1%	0.339	23.18

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表 6 多目标跟踪算法的消融实验结果

Table 6. Ablation experiment results of multi-object tracking algorithms

模型	IDF1	IDP	IDR	IDs	MOTA	MOTP	FPS
YOLOv8 + ByteTrack(Baseline)	65.5%	86.2%	52.8%	21.4	54%	0.387	24.92
YOLOv8 + DBB-RFAConv+AMSCEA	70.0%	87.4%	58.4%	18.4	58.7%	0.402	24.24
YOLOv8 + DBB-RFAConv + AMSCE-Attention + InnerPIoUv2(Ours)	71.5%	88.9%	59.8%	15.3	59.3%	0.393	24.05

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