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BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection.

Ruicheng Cao1, Ruiteng Zhang2, Xinyue Yan3

  • 1School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710072, China.

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|November 27, 2024
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Summary
This summary is machine-generated.

This study introduces BG-YOLO, a novel bidirectional guided method for underwater object detection. It enhances detection accuracy by optimizing image enhancement for object detection tasks without increasing computational cost.

Keywords:
feature guidedjoint optimizationunderwater image enhancementunderwater object detection

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Area of Science:

  • Computer Vision
  • Robotics
  • Marine Technology

Background:

  • Degraded underwater images significantly impair object detection accuracy.
  • Conventional image enhancement methods may degrade underwater object detection performance.

Purpose of the Study:

  • To propose a bidirectional guided method (BG-YOLO) for improving underwater object detection.
  • To enhance detection accuracy without introducing additional computational overhead.

Main Methods:

  • A parallel network structure with an image enhancement branch and an object detection branch.
  • A feature-guided module connecting shallow convolutional layers of both branches.
  • Training the enhancement branch using detection subnet guidance and consistency loss for the detection branch.

Main Results:

  • BG-YOLO significantly improves the detection performance of YOLOv5s, increasing mAP by up to 2.9%.
  • The method maintains the same inference speed (132 fps) as YOLOv5s during detection tasks.

Conclusions:

  • The bidirectional guided approach effectively enhances underwater object detection.
  • BG-YOLO offers a computationally efficient solution for improving marine object detection accuracy.