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 accessAbstract
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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“Enhanced YOLOv5 for detecting workpieces on production lines. Improved mAP on COCO dataset by 2.4% and on custom data by 4.2% using ghost bottleneck lightweight deep convolution.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [128]
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Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
Remote Sensing Object Detection Meets Deep Learning: A metareview of challenges and advances
Zhang, X., Zhang, T., Wang, G., Zhu, P., Tang, X., Jia, X., & Jiao, L. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3312347 · Cited by 219
Self-citationShip detection with deep learning: a survey
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LMSD-YOLO: A Lightweight YOLO Algorithm for Multi-Scale SAR Ship Detection
Guo, Y., Chen, S., Zhan, R., Wang, W., & Zhang, J. (2022) · Remote Sensing · doi:10.3390/rs14194801 · Cited by 133
Ship detection based on deep learning using SAR imagery: a systematic literature review
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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
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2022-02-20
- Volume
- 14
- Issue
- 4
- Pages
- 1018
- ISSN
- 2072-4292 (ISSN-L)
- 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
- 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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