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ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background

A synthetic aperture radar (SAR) image is crucial for ship detection in computer vision. Due to the background clutter, pose variations, and scale changes, it is a challenge to construct a SAR ship detection model with low false-alarm rates and high accuracy. Therefore, this paper proposes a novel S...

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Detalles Bibliográficos
Autores principales: Zhao, Kai, Lu, Ruitao, Wang, Siyu, Yang, Xiaogang, Li, Qingge, Fan, Jiwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272714/
https://www.ncbi.nlm.nih.gov/pubmed/37334169
http://dx.doi.org/10.3389/fnbot.2023.1170163
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author Zhao, Kai
Lu, Ruitao
Wang, Siyu
Yang, Xiaogang
Li, Qingge
Fan, Jiwei
author_facet Zhao, Kai
Lu, Ruitao
Wang, Siyu
Yang, Xiaogang
Li, Qingge
Fan, Jiwei
author_sort Zhao, Kai
collection PubMed
description A synthetic aperture radar (SAR) image is crucial for ship detection in computer vision. Due to the background clutter, pose variations, and scale changes, it is a challenge to construct a SAR ship detection model with low false-alarm rates and high accuracy. Therefore, this paper proposes a novel SAR ship detection model called ST-YOLOA. First, the Swin Transformer network architecture and coordinate attention (CA) model are embedded in the STCNet backbone network to enhance the feature extraction performance and capture global information. Second, we used the PANet path aggregation network with a residual structure to construct the feature pyramid to increase global feature extraction capability. Next, to cope with the local interference and semantic information loss problems, a novel up/down-sampling method is proposed. Finally, the decoupled detection head is used to achieve the predicted output of the target position and the boundary box to improve convergence speed and detection accuracy. To demonstrate the efficiency of the proposed method, we have constructed three SAR ship detection datasets: a norm test set (NTS), a complex test set (CTS), and a merged test set (MTS). The experimental results show that our ST-YOLOA achieved an accuracy of 97.37%, 75.69%, and 88.50% on the three datasets, respectively, superior to the effects of other state-of-the-art methods. Our ST-YOLOA performs favorably in complex scenarios, and the accuracy is 4.83% higher than YOLOX on the CTS. Moreover, ST-YOLOA achieves real-time detection with a speed of 21.4 FPS.
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spelling pubmed-102727142023-06-17 ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background Zhao, Kai Lu, Ruitao Wang, Siyu Yang, Xiaogang Li, Qingge Fan, Jiwei Front Neurorobot Neuroscience A synthetic aperture radar (SAR) image is crucial for ship detection in computer vision. Due to the background clutter, pose variations, and scale changes, it is a challenge to construct a SAR ship detection model with low false-alarm rates and high accuracy. Therefore, this paper proposes a novel SAR ship detection model called ST-YOLOA. First, the Swin Transformer network architecture and coordinate attention (CA) model are embedded in the STCNet backbone network to enhance the feature extraction performance and capture global information. Second, we used the PANet path aggregation network with a residual structure to construct the feature pyramid to increase global feature extraction capability. Next, to cope with the local interference and semantic information loss problems, a novel up/down-sampling method is proposed. Finally, the decoupled detection head is used to achieve the predicted output of the target position and the boundary box to improve convergence speed and detection accuracy. To demonstrate the efficiency of the proposed method, we have constructed three SAR ship detection datasets: a norm test set (NTS), a complex test set (CTS), and a merged test set (MTS). The experimental results show that our ST-YOLOA achieved an accuracy of 97.37%, 75.69%, and 88.50% on the three datasets, respectively, superior to the effects of other state-of-the-art methods. Our ST-YOLOA performs favorably in complex scenarios, and the accuracy is 4.83% higher than YOLOX on the CTS. Moreover, ST-YOLOA achieves real-time detection with a speed of 21.4 FPS. Frontiers Media S.A. 2023-06-02 /pmc/articles/PMC10272714/ /pubmed/37334169 http://dx.doi.org/10.3389/fnbot.2023.1170163 Text en Copyright © 2023 Zhao, Lu, Wang, Yang, Li and Fan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhao, Kai
Lu, Ruitao
Wang, Siyu
Yang, Xiaogang
Li, Qingge
Fan, Jiwei
ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title_full ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title_fullStr ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title_full_unstemmed ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title_short ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background
title_sort st-yoloa: a swin-transformer-based yolo model with an attention mechanism for sar ship detection under complex background
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272714/
https://www.ncbi.nlm.nih.gov/pubmed/37334169
http://dx.doi.org/10.3389/fnbot.2023.1170163
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