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: The 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)

Received Date: 2025-10-27
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 increased the significance of underwater object detection for resource exploration and environmental monitoring. Complex underwater environments often degrade image quality through color casts, haze-like effects, and non-uniform illumination. These factors reduce the performance of existing vision-based object detection algorithms, particularly for small objects, and often lead to missed detections and false positives. In addition, current deep learning–based underwater detection models face difficulty balancing detection accuracy and lightweight design under limited computational resources. Therefore, efficient underwater object detection methods are required for water-related vision tasks. Such methods support marine resource exploration, ecological monitoring, underwater robotics, and perception systems for autonomous underwater vehicles. Methods A lightweight framework based on redundant information optimization is proposed for underwater object detection. Specifically, a lightweight underwater object detection network, termed UWF-YOLO, is designed based on redundant information optimization. First, the C2f module is reconstructed using the FasterNet Block to optimize both the backbone and neck networks. A feature channel selection mechanism is integrated to reduce redundant feature representations. Furthermore, redundant convolutional features in the conventional YOLO neck limit adaptation to underwater environments. Therefore, Ghost Convolution is introduced to generate Ghost feature maps and improve the multi-scale feature fusion capability of the neck network. Next, parameter sharing is achieved by replacing the original detection head with a redundant optimization group detection head (RRG-Head) based on group convolution, which reduces computational cost. Finally, a structured channel pruning strategy is applied to identify inter-layer dependencies in the computational graph and bind pruning units. Combined with LAMP weight magnitude score normalization to evaluate channel importance, low-contributing groups are pruned and subsequently fine-tuned to compress the network size. In addition, existing underwater detection datasets usually contain monotonous scenes, and the objects are typically small and densely distributed. To address this limitation, an underwater object detection dataset with complex scenes, termed CSUOD, is constructed by collecting real-world underwater images from various websites and platforms. Manual annotation and resolution normalization are then performed to ensure dataset consistency. CSUOD is designed for challenging underwater environments characterized by color casts, haze-like effects, and non-uniform illumination. A total of 1 135 images containing six object categories are manually selected and annotated. Results and Discussions Extensive experiments are conducted on three public underwater object detection datasets, namely DUO, RUOD, and TrashCan, and several widely used detection methods are compared. The proposed model is evaluated against mainstream detectors, including YOLOv5s, YOLOv7-tiny, YOLOv8s, YOLOv9-tiny, and Deformable DETR. In terms of computational complexity, the proposed method reduces FLOPs, model size, and parameters by 60.4%, 77.3%, and 78.4%, respectively, compared with the baseline model. Furthermore, the proposed method outperforms YOLOv9-tiny with comparable parameters by 0.3%, 2.3%, and 3.4% in mAP on the three datasets. Additional comparative experiments on the constructed CSUOD dataset also demonstrate improved performance and stable detection capability in complex underwater environments. Qualitative visualization results further demonstrate the robustness and detection stability of the model under various underwater degradations, including haze-like effects and non-uniform illumination. Conclusions Quantitative and qualitative experiments on multiple datasets validate the effectiveness and robustness of the proposed method. The proposed framework achieves superior detection performance in complex underwater environments and reduces missed detections and false positives caused by background interference. Experimental results indicate that the proposed UWF-YOLO achieves significant model lightweighting while maintaining detection accuracy comparable to benchmark models. This balance between detection accuracy and low computational cost makes the framework suitable for underwater devices with limited resources. The proposed method also shows strong potential for practical applications such as marine ecological monitoring, underwater resource exploration, and perception systems for autonomous underwater vehicles. It provides a reliable technical foundation for real-time applications, supports integration into embedded platforms, and enables real-time perception and decision-making under different underwater conditions. In addition, the constructed CSUOD dataset helps address the limitations of existing underwater detection datasets and supports further research in underwater object detection. Future work will extend this framework to multi-modal perception systems and larger-scale datasets, enabling adaptive models for dynamic underwater scenarios and supporting 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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