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Cited by 230Advanced Neural Network ApplicationsAdvanced SAR Imaging TechniquesRemote-Sensing Image Classification

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

Open access
Xu, Xiaowo, Zhang, Xiaoling, Zhang, Tianwen·2022·Remote Sensing
DOI 10.3390/rs14041018
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Abstract

Synthetic aperture radar (SAR) satellites can provide microwave remote sensing images without weather and light constraints, so they are widely applied in the maritime monitoring field. Current SAR ship detection methods based on deep learning (DL) are difficult to deploy on satellites, because these methods usually have complex models and huge calculations. To solve this problem, based on the You Only Look Once version 5 (YOLOv5) algorithm, we propose a lightweight on-board SAR ship detector called Lite-YOLOv5, which (1) reduces the model volume; (2) decreases the floating-point operations (FLOPs); and (3) realizes the on-board ship detection without sacrificing accuracy. First, in order to obtain a lightweight network, we design a lightweight cross stage partial (L-CSP) module to reduce the amount of calculation and we apply network pruning for a more compact detector. Then, in order to ensure the excellent detection performance, we integrate a histogram-based pure backgrounds classification (HPBC) module, a shape distance clustering (SDC) module, a channel and spatial attention (CSA) module, and a hybrid spatial pyramid pooling (H-SPP) module to improve detection performance. To evaluate the on-board SAR ship detection ability of Lite-YOLOv5, we also transplant it to the embedded platform NVIDIA Jetson TX2. Experimental results on the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) show that Lite-YOLOv5 can realize lightweight architecture with a 2.38 M model volume (14.18% of model size of YOLOv5), on-board ship detection with a low computation cost (26.59% of FLOPs of YOLOv5), and superior detection accuracy (1.51% F1 improvement compared with YOLOv5).

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Research works printed but not linked: 4

  1. Mastromichalakis, S. (2020). ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance. arXiv.
  2. Gagnon, 1998, R&D activities in airborne SAR image processing/analysis at Lockheed Martin Canada, Proc. SPIE Int. Soc. Opt. Eng., 3491, 998
  3. Lin, M., Chen, Q., and Yan, S. (2013). Network in Network. arXiv.
  4. Zhang, X., Wan, F., Liu, C., and Ye, Q. (2019). FreeAnchor: Learning to match anchors for visual object detection. arXiv.

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Metadata

Authors
Xu, X., Zhang, X., & Zhang, T.
Affiliations
University of Electronic Science and Technology of China (CN)
Corresponding author
Zhang, Xiaoling
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2022-02-20
Volume
14
Issue
4
Pages
1018
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs14041018
OpenAlex
W4212781487
Cited by
221
References
58
Citations by year
2026: 21 · 2025: 37 · 2024: 67 · 2023: 61 · 2022: 35
FWCI
16.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Keywords
SAR ship detection, lightweight object detection, Sentinel-1, maritime surveillance, network pruning, channel and spatial attention, NVIDIA Jetson TX2, embedded deployment

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