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Related Experiment Video

Updated: Jun 13, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Dynamic Object Detection in Maritime Navigation Scenarios Based on Vision-Radar Fusion.

Qianqian Chen1, Changshi Xiao2, Bowei Li3

  • 1School of Artificial Intelligence and Big Data, Wuhan Business University, Wuhan 430056, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a vision-radar fusion method for accurate dynamic object detection in complex maritime settings, improving intelligent navigation systems. The approach enhances detection accuracy and robustness across various challenging environmental conditions.

Keywords:
deep learningdynamic object detectionintelligent navigationmulti-scale detectionvision–radar fusion

Related Experiment Videos

Last Updated: Jun 13, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Area of Science:

  • Maritime intelligent navigation
  • Computer vision
  • Sensor fusion

Background:

  • Accurate dynamic object detection is crucial for intelligent navigation but faces challenges like occlusion and scale variation in maritime environments.
  • Existing methods struggle with complex conditions such as fog and multi-target interference.

Purpose of the Study:

  • To develop a robust vision-radar fusion method for enhanced dynamic object detection in complex maritime scenarios.
  • To improve the accuracy and stability of object detection for intelligent navigation systems.

Main Methods:

  • A cross-modal feature mapping mechanism for deep visual-radar information integration.
  • Augmented Lagrangian optimization to improve feature consistency and representation.
  • An optimized Faster R-CNN framework with multi-scale training for varied object scales.

Main Results:

  • Achieved high detection accuracies: 88.93% (sunny), 76.86% (strong illumination), 74.47% (foggy), and 83.01% (crossing waterways).
  • Demonstrated strong robustness and stability in diverse and challenging maritime conditions.
  • Significantly improved dynamic object detection performance.

Conclusions:

  • The proposed vision-radar fusion method effectively addresses limitations in maritime object detection.
  • This approach provides reliable environmental perception for intelligent navigation systems.
  • The method shows significant potential for real-world applications in maritime safety and autonomy.