التفاصيل البيبلوغرافية
| العنوان: |
LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images |
| المؤلفون: |
Yang Tian, Xuan Wang, Shengjie Zhu, Fang Xu, Jinghong Liu |
| المصدر: |
Remote Sensing ; Volume 15 ; Issue 17 ; Pages: 4358 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
optical remote sensing, small-ship detection, lightweight detection, convolutional neural network |
| جغرافية الموضوع: |
agris |
| الوصف: |
Ship detection technology has achieved significant progress recently. However, for practical applications, lightweight ship detection still remains a very challenging problem since small ships have small relative scales in wide images and are easily missed in the background. To promote the research and application of small-ship detection, we propose a new remote sensing image dataset (VRS-SD v2) and provide a fog simulation method that reflects the actual background in remote sensing ship detection. The experiment results show that the proposed fog simulation is beneficial in improving the robustness of the model for extreme weather. Further, we propose a lightweight detector (LMSD-Net) for ship detection. Ablation experiments indicate the improved ELA-C3 module can efficiently extract features and improve the detection accuracy, and the proposed WGC-PANet can reduce the model parameters and computation complexity to ensure a lightweight nature. In addition, we add a Contextual Transformer (CoT) block to improve the localization accuracy and propose an improved localization loss specialized for tiny-ship prediction. Finally, the overall performance experiments demonstrate that LMSD-Net is competitive in lightweight ship detection among the SOTA models. The overall performance achieves 81.3% in AP@50 and could meet the lightweight and real-time detection requirements. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Remote Sensing Image Processing; https://dx.doi.org/10.3390/rs15174358 |
| DOI: |
10.3390/rs15174358 |
| الاتاحة: |
https://doi.org/10.3390/rs15174358 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.72F333E5 |
| قاعدة البيانات: |
BASE |