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Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion.

Yanling Han1, Liang Chen1, Yu Luo2

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.

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|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces FA-CenterNet, an improved underwater holothurian detection algorithm. It enhances accuracy for fuzzy and small targets by fusing scene context with EfficientNet-B3, achieving high performance with reduced computational cost.

Keywords:
CenterNetcontext informationholothurianscene feature fusiontransformerunderwater target detection

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Area of Science:

  • Computer Vision
  • Marine Biology
  • Robotics

Background:

  • Underwater images suffer from noise, low contrast, and color distortion.
  • Holothurian recognition is challenging due to ambiguous morphology, background similarity, and complex ecological scenes.

Purpose of the Study:

  • To propose an effective underwater holothurian target-detection algorithm (FA-CenterNet).
  • To address challenges in detecting holothurians in complex underwater environments.
  • To balance detection accuracy with computational efficiency for embedded systems.

Main Methods:

  • Utilized EfficientNet-B3 as a backbone network to minimize model size (Params) and computational load (FLOPs).
  • Developed a Feature Pyramid Transformer (FPT) module for multi-scale and spatial feature extraction from ecological scenarios.
  • Integrated an Attention Fusion Feature (AFF) module for deep fusion of shallow-detail and high-level semantic features.

Main Results:

  • Achieved an AP50 of 83.43% on the 2020 CURPC underwater dataset.
  • Demonstrated superior performance compared to other methods in holothurian detection.
  • Maintained a balance between accuracy, Params (15.90 M), and FLOPs (25.12 G).

Conclusions:

  • FA-CenterNet effectively improves the detection of fuzzy, small, and densely packed holothurians.
  • The algorithm is suitable for underwater holothurian detection in various scenarios.
  • The method offers a good trade-off between detection accuracy and computational resources.