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Related Experiment Videos

Underwater Robot Object Detection Algorithm Based on YOLOv11.

Yongqing Shi1, Wei Chen1, Duo Wan1

  • 1School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212003, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a degradation-aware YOLOv11s framework for robust underwater object detection. The enhanced model significantly improves accuracy in complex aquatic environments, meeting real-time robotic needs.

Keywords:
ROV underwater robotYOLOv11object detection performanceunderwater object detection

Related Experiment Videos

Area of Science:

  • Robotics and Computer Vision
  • Marine Technology
  • Artificial Intelligence

Background:

  • Underwater environments present significant challenges for object detection due to scattering, absorption, and illumination variations.
  • Traditional object detection models struggle with feature degradation in aquatic settings, limiting robotic applications.

Purpose of the Study:

  • To develop a robust object detection framework for underwater robotics that mitigates feature degradation.
  • To enhance the accuracy and real-time performance of underwater target recognition systems.

Main Methods:

  • Proposed a degradation-aware YOLOv11s framework incorporating spatial channel reconstruction convolutions (SCConv) and Sequential Reconstruction Units (SRU-CRU) for feature enhancement.
  • Integrated a Shuffle Attention module to improve channel-spatial feature interaction for fine-grained target representation.
  • Implemented Focaler-IoU for accurate and stable bounding-box regression.

Main Results:

  • The enhanced model achieved a mean Average Precision (mAP@0.5) of 88.4% in underwater robotic detection tasks.
  • Demonstrated a 3.2 percentage point improvement over the baseline YOLOv11s model.
  • Maintained real-time processing capabilities essential for underwater robotic operations.

Conclusions:

  • The proposed degradation-aware YOLOv11s framework effectively addresses feature degradation in underwater object detection.
  • The model offers improved accuracy and robustness for underwater robotic applications while preserving real-time performance.