LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an efficient YOLOv11 network module for high-precision, arbitrary-oriented ship detection in remote sensing images. The method improves accuracy and speed, outperforming existing state-of-the-art techniques.
Area Of Science
- Computer Vision
- Remote Sensing
- Artificial Intelligence
Background
- Arbitrary-oriented ship detection in remote sensing images is challenging due to high resolution, poor clarity, and scale variations.
- Existing methods often struggle to balance high accuracy and detection speed.
Purpose Of The Study
- To design a lightweight and efficient module for high-precision, arbitrary-oriented ship detection in remote sensing images.
- To improve the performance of the YOLOv11 network for this specific task.
Main Methods
- Developed a lightweight and efficient multi-scale feature dilated neck module integrated into the YOLOv11 network.
- Utilized multi-scale dilated attention to capture semantic details.
- Employed cross-stage partial networks for spatial-semantic information interaction.
- Introduced the GSConv module to preserve semantic information during feature transmission.
Main Results
- The proposed method demonstrates a lightweight structure and high accuracy.
- Achieved superior ship detection performance compared to state-of-the-art methods.
- On the HRSC2016 dataset, improvements of 3.1% mAP@0.5 and 3.3% mAP@0.5:0.95 were observed compared to YOLOv11n.
- On the MMShip dataset, improvements of 1.9% mAP@0.5 and 1.3% mAP@0.5:0.95 were achieved compared to YOLOv11n.
Conclusions
- The designed module effectively enhances arbitrary-oriented ship detection in remote sensing imagery.
- The method offers a promising solution for accurate and efficient ship detection, outperforming existing approaches.
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