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

HOU Guojia; MA Jiaqi; WANG Yuechuan; HUANG Baoxiang; LI Kunqian

doi:10.11999/JEIT251129

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HOU Guojia, MA Jiaqi, WANG Yuechuan, HUANG Baoxiang, LI Kunqian. UWF-YOLO: A Lightweight Framework for Underwater Object Detection via Redundant Information Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251129

Citation:

HOU Guojia, MA Jiaqi, WANG Yuechuan, HUANG Baoxiang, LI Kunqian. UWF-YOLO: A Lightweight Framework for Underwater Object Detection via Redundant Information Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251129

Citation:

HOU Guojia, MA Jiaqi, WANG Yuechuan, HUANG Baoxiang, LI Kunqian. UWF-YOLO: A Lightweight Framework for Underwater Object Detection via Redundant Information Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251129

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

doi: 10.11999/JEIT251129 cstr: 32379.14.JEIT251129

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)

Accepted Date: 2026-02-13
Rev Recd Date: 2026-02-13

Available Online: 2026-03-01

Abstract

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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References(24)

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