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A small underwater object detection model with enhanced feature extraction and fusion

  • 0School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Scientific Reports +

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January 18, 2025

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January 18, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an efficient deep learning model for detecting small underwater objects. The novel approach enhances feature retention and multi-scale extraction, improving detection accuracy and computational efficiency for marine monitoring.

Area Of Science

  • Marine Biology
  • Computer Vision
  • Deep Learning

Background

  • Small object detection is vital for underwater environmental protection and marine life monitoring.
  • Deep learning offers efficient detection techniques, but underwater challenges persist.
  • Underwater environments pose difficulties due to complexity, limited small object data, and computational constraints.

Purpose Of The Study

  • To develop an efficient deep convolutional network model for small object detection in underwater environments.
  • To address the challenges of feature retention, multi-scale extraction, and computational efficiency.

Main Methods

  • Introduced a CSP for small object and lightweight (CSPSL) module to enhance feature retention.
  • Proposed a variable kernel convolution (VKConv) for dynamic kernel size adjustment and multi-scale feature extraction.
  • Presented a spatial pyramid pooling for multi-scale (SPPFMS) method to preserve small object features.

Main Results

  • Ablation experiments on the UDD dataset confirmed the effectiveness of the proposed methods.
  • Comparative experiments on UDD and DUO datasets showed superior performance over state-of-the-art methods.
  • The model achieved the best performance in computational cost and detection accuracy for real-time tasks.

Conclusions

  • The proposed deep convolutional network model effectively addresses challenges in underwater small object detection.
  • The integration of CSPSL, VKConv, and SPPFMS modules significantly improves detection performance.
  • The model offers a promising solution for real-time, accurate underwater small object detection.

Keywords: