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Related Experiment Video

Updated: Jan 3, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Convolutional neural network based obstacle detection for unmanned surface vehicle.

Li Yong Ma1, Wei Xie1, Hai Bin Huang1

  • 1School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China.

Mathematical Biosciences and Engineering : MBE
|November 17, 2019
PubMed
Summary

This study introduces a new method for obstacle detection and classification in Unmanned Surface Vehicles (USV) using a hybrid neural network. The approach enhances autonomous navigation by improving accuracy in identifying marine obstacles.

Keywords:
convolutional neural networkdeep learningfeature pyramid networkobstacle detectionunmanned surface vehicle

Related Experiment Videos

Last Updated: Jan 3, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

973

Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Marine Engineering

Background:

  • Unmanned Surface Vehicles (USV) are crucial for future marine tasks, requiring robust autonomous navigation.
  • Obstacle avoidance is a key technology for USV safety and operational efficiency.
  • Current methods for obstacle detection and classification in USV visual data face accuracy limitations.

Purpose of the Study:

  • To enhance the detection and classification accuracy of obstacles for Unmanned Surface Vehicles (USV).
  • To develop an improved algorithm for environmental sensing in USV autonomous navigation.
  • To address the challenges of low accuracy in visual inspection tasks for USV.

Main Methods:

  • A novel bidirectional feature pyramid network was proposed, integrating ResNet and improved DenseNet architectures.
  • The hybrid network leverages multi-layer detail features and high-level semantic features for enhanced obstacle recognition.
  • The method was evaluated using a self-built dataset, ablation experiments, and performance tests on open datasets.

Main Results:

  • The proposed algorithm demonstrated superior performance in obstacle detection and classification compared to existing methods.
  • The integration of ResNet and DenseNet effectively combined detailed and semantic features for improved accuracy.
  • Experimental results confirmed the algorithm's suitability for enhancing USV autonomous navigation systems.

Conclusions:

  • The developed bidirectional feature pyramid network significantly improves obstacle detection and classification for USV.
  • This advancement is critical for enabling more reliable and effective autonomous navigation in diverse marine environments.
  • The proposed method offers a promising solution for enhancing the perception capabilities of future USV.