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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Published on: February 12, 2014

A Dynamic Multi-Scale Feature Fusion Network for Enhanced SAR Ship Detection.

Rui Cao¹, Jianghua Sui¹

¹Navigation and Ship Engineering College, Dalian Ocean University, Dalian 116023, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This study introduces DRGD-YOLO, an enhanced algorithm improving synthetic aperture radar (SAR) ship detection in complex marine environments. The new method significantly boosts detection accuracy and robustness, offering potential for maritime surveillance and safety.

Keywords:

CSP_DTB DYDDH RepGFPN SAR ship detection YOLOv11

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

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Published on: December 15, 2023

Area of Science:

Computer Vision
Remote Sensing
Artificial Intelligence

Background:

Current synthetic aperture radar (SAR) ship detection methods struggle with complex marine environments, facing challenges like environmental interference, false detections, and multi-scale target variations.
Existing algorithms often lack the robustness required for accurate and reliable ship identification in diverse sea conditions.

Purpose of the Study:

To develop an enhanced YOLO algorithm, named DRGD-YOLO, for superior ship detection performance using synthetic aperture radar (SAR) data.
To address limitations in current SAR ship detection techniques by integrating multi-level feature fusion and dynamic detection mechanisms.

Main Methods:

Designed a cross-stage partial dynamic channel transformer module (CSP_DTB) to enhance feature extraction by combining transformer and convolutional neural network architectures.
Introduced a general dynamic feature pyramid network (RepGFPN) for efficient multi-scale feature fusion and information propagation within the model's neck architecture.
Developed a lightweight dynamic decoupled dual-alignment head (DYDDH) to improve the collaborative performance of localization and classification tasks.

Main Results:

The DRGD-YOLO algorithm achieved an mAP50 of 93.1% and mAP50-95 of 69.2% on the HRSID dataset.
Demonstrated significant performance improvements over the baseline YOLOv11n, with increases of 3.3% in mAP50 and 4.6% in mAP50-95.
The enhanced algorithm shows improved accuracy and robustness in synthetic aperture radar (SAR) ship detection.

Conclusions:

The proposed DRGD-YOLO algorithm offers a substantial advancement in SAR ship detection accuracy and robustness.
The method has broad application potential in maritime surveillance, fisheries management, and maritime safety monitoring.
This research provides crucial technical support for the advancement of intelligent marine monitoring technologies.