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Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task | |
2022-05-01 | |
发表期刊 | SENSORS |
ISSN | 1424-8220 |
EISSN | 1424-8220 |
卷号 | 22期号:9 |
发表状态 | 已发表 |
DOI | 10.3390/s22093370 |
摘要 | The real-time performance of ship detection is an important index in the marine remote sensing detection task. Due to the computing resources on the satellite being limited by the solar array size and the radiation-resistant electronic components, information extraction tasks are usually implemented after the image is transmitted to the ground. However, in recent years, the one-stage based target detector such as the You Only Look Once Version 5 (YOLOv5) deep learning framework shows powerful performance while being lightweight, and it provides an implementation scheme for on-orbit reasoning to shorten the time delay of ship detention. Optimizing the lightweight model has important research significance for SAR image onboard processing. In this paper, we studied the fusion problem of two lightweight models which are the Coordinate Attention (CA) mechanism module and the YOLOv5 detector. We propose a novel lightweight end-to-end object detection framework fused with a CA module in the backbone of a suitable position: YOLO Coordinate Attention SAR Ship (YOLO-CASS), for the SAR ship target detection task. The experimental results on the SSDD synthetic aperture radar (SAR) remote sensing imagery indicate that our method shows significant gains in both efficiency and performance, and it has the potential to be developed into onboard processing in the SAR satellite platform. The techniques we explored provide a solution to improve the performance of the lightweight deep learning-based object detection framework. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
关键词 | Deep learning Object detection Orbits Radar imaging Remote sensing Satellite imagery Ships Synthetic aperture radar Tracking radar Vehicle performance Attention mechanisms Coordinate attention mechanism Detection tasks On-board processing Onboard computing Performance Real time performance Ship detection Ship object detection You only look once version 5 |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
WOS类目 | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000794488400001 |
出版者 | MDPI |
EI入藏号 | 20221812051734 |
EI主题词 | Object recognition |
EI分类号 | 461.4 Ergonomics and Human Factors Engineering ; 655.2 Satellites ; 662.1 Automobiles ; 663.1 Heavy Duty Motor Vehicles ; 716.2 Radar Systems and Equipment ; 723.2 Data Processing and Image Processing |
原始文献类型 | Journal article (JA) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/180923 |
专题 | 信息科学与技术学院_特聘教授组_林宝军组 |
通讯作者 | Lin, Baojun |
作者单位 | 1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China 2.Univ Chinese Acad Sci, Sch Optoelect, Beijing 100094, Peoples R China 3.Chinese Acad Sci, Innovat Acad Microsatellites, Shanghai 201210, Peoples R China 4.Shanghai Engn Ctr Microsatellites, Shanghai 201304, Peoples R China 5.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China |
通讯作者单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Xie, Fang,Lin, Baojun,Liu, Yingchun. Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task[J]. SENSORS,2022,22(9). |
APA | Xie, Fang,Lin, Baojun,&Liu, Yingchun.(2022).Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task.SENSORS,22(9). |
MLA | Xie, Fang,et al."Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task".SENSORS 22.9(2022). |
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