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

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

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Xu, Xiaowo, Zhang, Xiaoling, Zhang, Tianwen·2022·Remote Sensing

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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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Authors: Xu, X., Zhang, X., & Zhang, T.
Affiliations: University of Electronic Science and Technology of China (CN)
Corresponding author: Zhang, Xiaoling
ORCID: Xu, Xiaowo
Zhang, Xiaoling
Zhang, Tianwen
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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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Lite-YOLOv5: A Lightweight Deep Learning Detector for On-Board Ship Detection in Large-Scene Sentinel-1 SAR Images

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