LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

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...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 17; p. 4358
Main Authors Tian, Yang, Wang, Xuan, Zhu, Shengjie, Xu, Fang, Liu, Jinghong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2023
Subjects
Ablation
Accuracy
Computational linguistics
convolutional neural network
Detectors
Extreme weather
Fog
Global positioning systems
GPS
Language processing
Lightweight
lightweight detection
Localization
Military deployment
Natural language interfaces
Network management systems
optical remote sensing
Remote sensing
Semantics
Sensors
Simulation methods
small-ship detection
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15174358