UWF-YOLO: 冗余信息优化的轻量化水下目标检测

侯国家; 马佳琦; 王岳川; 黄宝香; 李坤乾

doi:10.11999/JEIT251129

UWF-YOLO: 冗余信息优化的轻量化水下目标检测

doi: 10.11999/JEIT251129 cstr: 32379.14.JEIT251129

侯国家^{1, 2},
马佳琦¹,
王岳川¹,
黄宝香^{1, 2},
李坤乾^3, ,

1.
青岛大学计算机科学技术学院青岛 266071
2.
青岛海洋科技中心区域海洋动力学与数值模拟功能实验室青岛 266237
3.
中国海洋大学工程学院青岛 266404

基金项目: 国家自然科学基金 (62371431, 61901240)，青岛市自然科学基金 (24-4-4-zrjj-122-jch)，山东省自然科学基金 (ZR2024MF125, ZR2025QB60)

详细信息

作者简介:
侯国家：男，博士，副教授，研究方向为图像/视频处理、海洋视觉感知与理解

马佳琦：女，博士生，研究方向为计算机视觉

王岳川：男，硕士生，研究方向为图像处理

黄宝香：女，博士，教授，研究方向为大数据海洋学、遥感图像处理、人工智能

李坤乾：男，博士，副教授，研究方向为计算机视觉，海洋装备人工智能技术

通讯作者:
李坤乾　likunqian@ouc.edu.cn

中图分类号: TP391.41
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- 被引次数: 0
出版历程
- 修回日期: 2026-02-13
- 录用日期: 2026-02-13
- 网络出版日期: 2026-03-01

UWF-YOLO: A Lightweight Framework for Underwater Object Detection via Redundant Information Optimization

HOU Guojia^{1, 2},
MA Jiaqi¹,
WANG Yuechuan¹,
HUANG Baoxiang^{1, 2},
LI Kunqian^{3
, ,}

1.
College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
2.
Laboratory for Regional Oceanography and Numerical Modeling, Qingdao Marine Science and Technology Center, Qingdao 266237, China
3.
College of Engineering, Ocean University of China, Qingdao 266404, China

Funds: National Natural Science Foundation of China (62371431, 61901240), Qingdao Natural Science Foundation (24-4-4-zrjj-122-jch), Natural Science Foundation of Shandong Province, China (ZR2024MF125, ZR2025QB60)

摘要

摘要: 针对现有水下目标检测方法在成像退化类型多样与背景干扰等复杂场景中鲁棒性差以及在设备资源受限条件下难以兼顾检测精度与模型轻量化的问题，本文提出基于冗余信息优化的轻量化水下目标检测网络(Underwater Faster YOLO Network Based on Redundancy Information Optimization, UWF-YOLO)，并进一步构建了复杂场景水下目标检测数据集(Underwater Object Detection Dataset with Complex Scene, CSUOD)。UWF-YOLO采用FasterNet Block重构C2f模块优化主干和颈部网络，通过特征通道选择机制减少冗余特征，并引入Ghost卷积增强颈部网络的多尺度特征融合能力；同时，通过基于分组卷积的参数共享检测头降低计算开销；最后，应用结构化通道剪枝技术进一步压缩网络规模。CSUOD数据集通过收集真实水下图像标注并进行分辨率标准化处理，覆盖雾化、色偏、非均匀照明等各种退化类型，可用于复杂场景下水下目标检测模型的鲁棒性训练与性能评测。在DUO，RUOD和TrashCan数据集上进行实验表明，相较于YOLOv8s，所提方法在计算量、权重大小与参数量三个指标上的分别降低了60.4%、77.3%和78.4%；与参数量相当的YOLOv9-tiny相比，mAP指标在三个数据集上分别提升了0.3%、2.3%和3.4%。同时，在自建CSUOD数据集上的主客观对比实验，进一步证实所提模型在实现显著轻量化的同时，能够有效避免背景干扰导致的误检、漏检等问题，特别在复杂水下环境中展现出优异的检测性能。此外，本文构建的复杂场景水下数据集将有助于推动水下目标检测方法的发展。
- 水下目标检测 /
- 冗余信息优化 /
- 轻量化 /
- CSUOD数据集
Abstract: Objective The rapid development of underwater imaging technology has significantly elevated the importance of underwater object detection for resource exploration and environmental monitoring applications. Generally, complex underwater environments yield various degradations of image quality such as color casts, haze-like effects, and non-uniform illumination. Unfortunately, existing vision-based object detection algorithms always suffer from unpleasing performance and notable limitations especially for detecting small objects, resulting in missed detections and false positives. Moreover, existing deep learning based underwater detection models also face substantial challenges in striking an optimal balance between accuracy and lightweight design under the condition of limited equipment resources. To address these issues, it is of great importance to design efficient underwater object detection methods in view of water-related vision tasks, which play a crucial role in marine resource exploration, ecological monitoring, underwater robotics, and intelligent perception systems for autonomous underwater vehicles. Methods In this paper, we propose a novel lightweight framework based on redundant information optimization for underwater object detection. Technically, we propose a lightweight underwater object detection network called UWF-YOLO based on redundancy information optimization. First, the C2f module is reconstructed by FasterNet Block to optimize both the backbone and neck networks, and a feature channel selection mechanism is incorporated to reduce the redundant features. On other hand, due to the redundant traditional convolutional features in the YOLO neck, it is difficult to adapt to the underwater environment. Ghost Convolution is also introduced to generate the Ghost feature map for enhancing the multi-scale feature fusion capability of the neck network. Next, our proposed method achieves parameter sharing by replacing the original detection head with a redundant optimization group detection head (RRG-Head) based on group convolution, thereby reducing computational costs. Finally, the structured channel pruning technique is applied to identify the inter-layer dependencies of the graph and bind the pruning units. Combined with the LAMP weight magnitude score normalization for evaluating the importance of channels, the low-contributing groups are pruned and fine-tuned to achieve network size compression. In addition, since the scene of underwater detection datasets are typically monotonous and the underwater objects contained in the available datasets are usually small and clustered. We also construct an underwater object detection dataset with complex scene, namely CSUOD, by collecting real-world underwater images from different websites and platforms to ensure both its diversity and authenticity, followed by manual annotation and resolution normalization preprocessing. CSUOD is specifically designed for various challenging underwater environments characterized by color casts, haze-like effects, and non-uniform illumination. In our CSUOD, we manually select 1135 images containing 6 different types, and perform the manual annotation and resolution standardization operations. Results and Discussions Extensive experiments are conducted on three public underwater object detection datasets (i.e., DUO, RUOD, and TrashCan) by comparing several popular and widely used object detection methods. The proposed model is evaluated against mainstream detectors, including YOLOv5s, YOLOv7-tiny, YOLOv8s, YOLOv9-tiny, and Deformable DETR. In computational complexity assessment, experimental results show that the proposed method has reduced the FLOPs, model size, and parameters by 60.4%, 77.3%, and 78.4%, respectively, compared to the baseline. In addition, our method has outperformed YOLOv9-tiny with comparable parameters by 0.3%, 2.3%, and 3.4% in mAP across the three datasets. Also, some comparative results on our established CSUOD dataset also indicate that our proposed model has a good improvement and stability even in complex underwater environments. Qualitative visualization results further illustrate the model’s robustness and detection stability under various underwater degradations, such as haze-like effects and non-uniform illumination. Conclusions Quantitative and qualitative experiments on different datasets have validated the effectiveness and robustness of the proposed method. In addition, our method achieves superior detection performance in complex underwater environments, effectively solving missed detections and false positives caused by background interference. A large number of experimental results show that our designed UWF-YOLO can not only achieve significant light weighting, but also maintain the comparable detection accuracy comparing with the benchmark model. This balance between the detection accuracy and low computational cost makes it particularly suitable for underwater devices with limited resources. Besides, the proposed method has great potential in practical scenarios such as marine ecological monitoring, underwater resource exploration, and autonomous underwater vehicle perception systems. It also provides a reliable and efficient technical foundation for real-time applications, with strong adaptability to different underwater conditions, efficient integration into embedded platforms, and support for real-time perception and decision-making. Our constructed dataset CSUOD in this study will help address the limitations of existing underwater object detection datasets and promote the development of underwater object detection. In the future, this work can be further extended to multi-modal perception systems and larger-scale datasets. These efforts will enable adaptive models for more dynamic underwater scenarios and support broader applications in intelligent ocean observation and autonomous navigation.
- Underwater object detection /
- Redundant information optimization /
- Lightweight framework /
- CSUOD dataset

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图 1 UWF-YOLO网络整体架构

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图 2 主干网络与高效跨阶段特征优化模块(ECFO)

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图 3 颈部网络与Ghost卷积

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图 4 冗余优化分组检测头(RRG-Head)

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图 5 CSUOD数据集部分样例

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图 6 数据集目标数量与类别比例分布

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图 7 各目标检测网络在不同数据集上的可视化对比结果

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表 1 DUO、RUOD、TrashCan数据集客观指标对比

网络	mAP50(%)			FLOPs(G)			Weight(M)			Params(M)
网络	DUO	RUOD	TrashCan	DUO	RUOD	TrashCan	DUO	RUOD	TrashCan	DUO	RUOD	TrashCan
YOLOv5s	82.3	85.9	89.2	15.8	15.8	15.9	14.4	14.5	14.5	7.0	7.0	7.1
Deformable DETR	80.1	83.3	81.4	51.1	51.1	51.1	480.0	480.0	480.0	39.8	39.8	39.8
YOLOv7-tiny	83.6	84.6	84.7	13.0	13.1	13.2	12.4	12.3	12.4	6.0	6.0	6.1
YOLOv8s	83.1	86.5	89.6	28.4	28.5	28.5	22.5	22.6	22.5	11.1	11.1	11.1
YOLOv9-tiny	82.8	84.3	86.0	10.7	10.7	10.7	6.1	6.1	6.1	2.6	2.6	2.6
UWF-YOLO (剪枝前)	84.2	87.0	90.1	19.5	19.5	19.5	14.7	14.7	14.7	7.2	7.2	7.2
UWF-YOLO	83.1	86.6	89.4	11.3	11.4	11.3	5.1	5.0	5.1	2.4	2.4	2.4

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表 2 自建CSUOD数据集指标对比

网络	mAP50_all (%)	AP_j (%)	AP_c (%)	AP_f (%)	AP_s (%)	AP_d (%)	AP_t (%)
YOLOv5s	78.4	86.7	81.5	76.9	50.8	87.1	87.8
YOLOv7-tiny	65.3	56.6	70.2	74.0	52.6	72.6	65.6
YOLOv7	79.6	89.6	82.5	80.5	61.6	81.6	81.8
YOLOv8s	78.0	85.9	72	79.6	64.2	76.4	90
YOLOv9-tiny	65.1	76.5	63.1	64.9	45.6	65.9	74.4
UWF-YOLO	79.6	84.2	79.8	75.2	70.5	84.2	84.1

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表 3 消融实验

数据集	方案	基线模型	ECFO	Ghost	RRG-Head	通道剪枝	mAP50 (%)	Params (M)	FLOPs (G)	Weight (M)
RUOD	1	√					86.5	11.1	28.5	22.6
	2	√	√				86.7	9.4	27.0	19.0
	3	√	√	√			86.7	9.0	26.6	18.3
	4	√	√	√	√		87.0	7.2	19.5	14.7
	5	√	√	√	√	√	86.6	2.4	11.4	5.0
TrashCan	1	√					89.6	11.1	28.5	22.5
	2	√	√				90.0	9.4	27.1	19.0
	3	√	√	√			90.2	9.0	26.6	18.3
	4	√	√	√	√		90.1	7.2	19.5	14.7
	5	√	√	√	√	√	89.4	2.4	11.3	5.1

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