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Related Experiment Video

Updated: May 28, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Structure-Enhanced Underwater Object Detection via Wavelet-Edge Collaboration and Selective Multi-Scale Fusion.

Dejun Li1, Chunrong He1, Peng Tu2

  • 1State Key Laboratory of Deepsea Manned Vehicle, China Ship Scientific Research Center, Wuxi 214082, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

A new underwater object detector, WEC-UOD, enhances structural details and fuses multi-scale features to improve detection accuracy in challenging marine environments. It outperforms existing baselines on benchmark datasets.

Area of Science:

  • Marine Biology
  • Computer Vision
  • Robotics

Background:

  • Underwater object detection is crucial for ocean exploration and marine applications.
  • Degraded image quality (absorption, scattering, interference) hinders detection of small or varied objects.

Purpose of the Study:

  • To develop an improved underwater object detector (WEC-UOD) that enhances structural representation and feature fusion.
  • To address challenges posed by poor underwater image quality without a separate enhancement step.

Main Methods:

  • Proposed WEC-UOD detector with a Wavelet-Edge Collaboration (WEC) module for structural compensation and edge refinement.
  • Implemented a Scale-Selective Fusion (SSF) module for adaptive feature aggregation and background suppression.
Keywords:
WEC-UODedge-guided spatial correctionscale-selective fusionunderwater object detectionwavelet subband compensation

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Last Updated: May 28, 2026

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Published on: December 15, 2023

Main Results:

  • WEC-UOD achieved mAP@0.5 scores of 87.4% on RUOD and 86.9% on DUO.
  • Consistently outperformed the YOLOv11s baseline on both datasets.

Conclusions:

  • The WEC-UOD effectively combines structural enhancement with selective multi-scale feature aggregation for superior underwater object detection.
  • The proposed modules significantly improve performance in challenging underwater conditions.