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Underwater Distortion Target Recognition Network (UDTRNet) via Enhanced Image Features.

Lei Cai1, Chuang Chen2, Haojie Chai1

  • 1School of Artificial Intelligence, Henan Institute of Science and Technology, Xinxiang 453003, China.

Computational Intelligence and Neuroscience
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel network for autonomous underwater vehicle (AUV) target recognition, improving feature extraction in challenging underwater conditions. The new method enhances recognition accuracy for distorted underwater images.

Area of Science:

  • Robotics
  • Computer Vision
  • Oceanography

Background:

  • Autonomous underwater vehicles (AUVs) struggle with target recognition due to limited labeled data and complex underwater environments.
  • Light refraction and environmental complexity hinder AUVs from extracting complete target features.

Purpose of the Study:

  • To propose an underwater distortion target recognition network (UDTRNet) to enhance image features for AUVs.
  • To address the challenges of target recognition in data-scarce and visually complex underwater settings.

Main Methods:

  • Utilized info noise contrastive estimation (InfoNCE) loss to extract significant image features.
  • Constructed a dynamic correlation matrix to capture and extract spatial semantic features of underwater targets.
  • Fused significant and spatial semantic features for model training using cross-entropy loss.

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Main Results:

  • The proposed UDTRNet demonstrated improved feature enhancement for underwater images.
  • Achieved a 1.52% increase in mean average precision (mAP) for recognizing underwater blurred images.

Conclusions:

  • The UDTRNet effectively enhances image features, leading to improved target recognition for AUVs in challenging underwater environments.
  • The integration of InfoNCE loss and dynamic correlation matrix offers a robust solution for underwater object detection.