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Lightweight underwater object detection method based on multi-scale edge information selection.

Shaobin Cai1, Xin Zhou2, Wanchen Cai3

  • 1College of Informaton Engineering, Huzhou University, Huzhou, 313000, Zhejiang, China. caishaobin@zjhu.edu.cn.

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This study introduces MAW-YOLOv11, a lightweight underwater object detector that enhances marine biodiversity analysis. It improves detection accuracy and efficiency in challenging underwater conditions, outperforming existing YOLOv11 models.

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Area of Science:

  • Marine Biology
  • Computer Vision
  • Image Processing

Background:

  • Underwater object detection is crucial for marine ecosystem monitoring but faces challenges like poor image quality due to lighting, distortion, and noise.
  • Limited computational resources in underwater equipment hinder efficient processing of visual data.
  • The YOLO algorithm is commonly used for underwater object detection, but improvements are needed for robustness and efficiency.

Purpose of the Study:

  • To propose a novel lightweight underwater object detection model, MAW-YOLOv11, designed for enhanced accuracy and efficiency in marine environments.
  • To address the limitations of existing underwater detection methods by incorporating multi-scale edge information selection and optimized processing techniques.
  • To improve the detection of critical underwater targets despite image quality degradation.

Main Methods:

  • Utilized dark channel prior for image dehazing and clarity enhancement.
  • Introduced a Multi-Scale Edge Information Select (MSEIS) module and C3kMSEIS module to extract and select multi-scale edge features.
  • Integrated an ADown downsampling structure to reduce computational load and employed WIoUv3 loss for improved handling of low-quality samples.

Main Results:

  • The MAW-YOLOv11 model achieved a mean Average Precision (mAP) of 81.4% on the URPC dataset, a 2.1% improvement over YOLOv11.
  • The model has a reduced parameter count of 2.11M, which is 0.47M less than YOLOv11, indicating improved efficiency.
  • Comparative experiments confirmed the effectiveness and superiority of MAW-YOLOv11 against other mainstream object detection algorithms.

Conclusions:

  • MAW-YOLOv11 demonstrates significant improvements in underwater object detection accuracy and efficiency.
  • The proposed multi-scale edge information selection and optimization techniques effectively address the challenges of underwater imaging.
  • This lightweight model offers a promising solution for robust marine biodiversity monitoring and underwater exploration with limited resources.