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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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FEB-YOLOv8: A multi-scale lightweight detection model for underwater object detection.

Yuyin Zhao1, Fengjie Sun1, Xuewen Wu1

  • 1Department of Cyberspace Security, Hainan University, Haikou, Hainan Province, China.

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This study introduces FEB-YOLOv8, a lightweight underwater object detection model addressing robot limitations. It achieves improved accuracy and reduced computational load, offering an efficient solution for marine resource management.

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Area of Science:

  • Computer Vision
  • Robotics
  • Marine Technology

Background:

  • Underwater object detection is vital for marine resource management.
  • Underwater robots face challenges with limited storage and computational power.
  • Existing detection models often struggle with efficiency and accuracy in marine environments.

Purpose of the Study:

  • To develop a novel lightweight object detection model for underwater robots.
  • To enhance the efficiency and accuracy of underwater object detection systems.
  • To address the constraints of limited storage and computational power in underwater robotic applications.

Main Methods:

  • Proposes FEB-YOLOv8, a lightweight model based on the YOLOv8 framework.
  • Enhances the backbone network with refined C2f and novel P-C2f modules.
  • Incorporates the EMA module to improve multi-scale feature extraction and a Bi-FPN-inspired feature pyramid network for balanced performance.

Main Results:

  • FEB-YOLOv8 achieved a 1.2% and 1.3% increase in mAP on DUO and URPC2020 datasets, respectively.
  • Reduced computational load with 6.2G GFLOPs (24.39% decrease) and 1.64M parameters (45.51% decrease) compared to the baseline.
  • Demonstrated a significant improvement in the balance between model lightness and detection precision.

Conclusions:

  • FEB-YOLOv8 offers an advantageous solution for underwater object detection.
  • The model effectively harmonizes lightness with accuracy, suitable for resource-constrained underwater robots.
  • The proposed modifications enhance feature extraction and detection performance in marine environments.