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Related Experiment Video

Updated: Feb 14, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
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Restoring Ambiguous Boundaries: An Efficient and Robust Framework for Underwater Camouflaged Object Detection.

Zihan Wei1, Yucheng Zheng1, Yaohua Shen1

  • 1Automation College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary

CAR-YOLO, a new lightweight detector, enhances underwater camouflaged object detection by resolving boundary ambiguity. It achieves state-of-the-art performance with improved efficiency.

Keywords:
boundary ambiguityfrequency-domain analysislightweight object detectionrobustness in degraded environmentsunderwater camouflaged object

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Underwater Camouflaged Object Detection (UCOD) faces challenges due to boundary ambiguity and optical degradation.
  • Biological mimicry and aquatic environments obscure target edges, hindering detection accuracy.

Purpose of the Study:

  • To develop a lightweight detector, CAR-YOLO (Camouflage Ambiguity Resolution YOLO), for improved UCOD.
  • To effectively address boundary ambiguity and enhance object detection in complex underwater scenes.

Main Methods:

  • Proposed a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) for high-frequency edge recovery.
  • Introduced a Low-level Adaptive Fusion (LAF) module to integrate low-frequency semantic information.
  • Implemented an Uncertainty Calibration Head (UCH) to refine supervision using prediction consistency.

Main Results:

  • CAR-YOLO achieved 27.1% mAP50-95 on UCOD10K, outperforming SOTA methods with reduced parameters and GFLOPs.
  • Attained 30.7% mAP50-95 on UWB-COT220, a 7.7-point improvement over YOLOv11.
  • Demonstrated strong generalization capabilities through cross-domain experiments on UODD.

Conclusions:

  • CAR-YOLO effectively mitigates boundary ambiguity in UCOD.
  • The model achieves a superior balance of accuracy, robustness, and computational efficiency.