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

Xu, Xiaowo; Zhang, Xiaoling; Zhang, Tianwen

doi:10.3390/rs14041018

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

Open this paper in CoChat — free account Open PDF

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).

Integrity signals

Registry

Crossref · 10.3390/rs14041018

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Enhanced YOLOv5 for detecting workpieces on production lines improved mAP by 2.4% on COCO dataset and 4.2% on custom data using ghost bottleneck lightweight deep convolution.
“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]

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Untitled work
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
Path Aggregation Network for Instance Segmentation
Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841 · cited 3×
“Tan, M.; Pang, R.; Le, Q.V. EfficientDet: Scalable and efficient object detection. arXiv 2019, arXiv:1911.09070.”
MethodInfluential
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., & Yeh, I.-H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00203 · Cited by 5,265
GhostNet: More Features From Cheap Operations
Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., & Xu, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00165 · Cited by 5,222
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034
Self-citation
An Introduction to Deep Learning for the Physical Layer
O'Shea, T., & Hoydis, J. (2017) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2017.2758370 · Cited by 2,980
Learning Efficient Convolutional Networks through Network Slimming
Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., & Zhang, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.298 · Cited by 2,761
Libra R-CNN: Towards Balanced Learning for Object Detection
Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00091 · Cited by 1,898
FoveaBox: Beyound Anchor-Based Object Detection
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., & Shi, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3002345 · Cited by 1,141
Recent advances in deep learning for object detection
Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.085 · Cited by 1,047
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis
Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., Su, H., et al. (2021) · Remote Sensing · doi:10.3390/rs13183690 · Cited by 796
Self-citation
A Forest Fire Detection System Based on Ensemble Learning
Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021) · Forests · doi:10.3390/f12020217 · Cited by 602
A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds
Wang, Y., Wang, C., Zhang, H., Dong, Y., & Wei, S. (2019) · Remote Sensing · doi:10.3390/rs11070765 · Cited by 546
Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges
Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2899085 · Cited by 525
Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images
Cui, Z., Li, Q., Cao, Z., & Liu, N. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2923988 · Cited by 500
Introduction to PyTorch
Ketkar, N. S. (2017) · Apress eBooks · doi:10.1007/978-1-4842-2766-4_12 · Cited by 497
Group sparse regularization for deep neural networks
Scardapane, S., Comminiello, D., Hussain, A., & Uncini, A. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.02.029 · Cited by 467
DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection
Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., & Wang, R. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.067 · Cited by 392
Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images
Lin, Z., Ji, K., Leng, X., & Kuang, G. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2882551 · Cited by 381
Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery
Wang, Y., Wang, C., Zhang, H., Dong, Y., & Wei, S. (2019) · Remote Sensing · doi:10.3390/rs11050531 · Cited by 364
Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection
Kang, M., Ji, K., Leng, X., & Lin, Z. (2017) · Remote Sensing · doi:10.3390/rs9080860 · Cited by 338
Ship Detection Based on YOLOv2 for SAR Imagery
Chang, Y.-L., Amare, A., Chang, L., Wang, Y. C., Hsiao, C.-Y., & Lee, W.-H. (2019) · Remote Sensing · doi:10.3390/rs11070786 · Cited by 333
LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images
Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., Pan, D., Li, J., Su, H., Zhou, Y., et al. (2020) · Remote Sensing · doi:10.3390/rs12182997 · Cited by 313
Self-citation
A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection
Jiao, J., Zhang, Y., Sun, H., Yang, X., Gao, X., Hong, W., Fu, K., & Sun, X. (2018) · IEEE Access · doi:10.1109/access.2018.2825376 · Cited by 298
Statistical Modeling of SAR Images: A Survey
Gao, G. (2010) · Sensors · doi:10.3390/s100100775 · Cited by 278
Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection
Zhang, T., Zhang, X., & Ke, X. (2021) · Remote Sensing · doi:10.3390/rs13142771 · Cited by 213
Self-citation
Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection
Zhang, T., Zhang, X., Shi, J., & Wei, S. (2019) · Remote Sensing · doi:10.3390/rs11212483 · Cited by 207
Self-citation
DRBox-v2: An Improved Detector With Rotatable Boxes for Target Detection in SAR Images
An, Q., Pan, Z., Liu, L., & You, H. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2920534 · Cited by 163
Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet
Wei, S., Su, H., Ming, J., Wang, C., Yan, M., Kumar, D., Shi, J., & Zhang, X. (2020) · Remote Sensing · doi:10.3390/rs12010167 · Cited by 163
Self-citation
Automatic Detection of Ships in RADARSAT-1 SAR Imagery
Wackerman, C. C., Friedman, K. S., Pichel, W. G., Clemente‐Colón, P., & Li, X. (2001) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2001.10854896 · Cited by 154
Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation
Chen, S., Zhan, R., Wang, W., & Zhang, J. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3041783 · Cited by 121
On Multiple Covariance Equality Testing with Application to SAR Change Detection
Ciuonzo, D., Carotenuto, V., & Maio, A. D. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2712124 · Cited by 100
On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
Xu, P., Li, Q., Zhang, B., Wu, F., Zhao, K., Du, X., Yang, C., & Zhong, R. (2021) · Remote Sensing · doi:10.3390/rs13101995 · Cited by 91
Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression
Wang, J., Lu, C., & Jiang, W. (2018) · Sensors · doi:10.3390/s18092851 · Cited by 89
Stacked Fisher autoencoder for SAR change detection
Liu, G., Li, L., Jiao, L., Dong, Y., & Li, X. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.106971 · Cited by 82
Efficient Low-Cost Ship Detection for SAR Imagery Based on Simplified U-Net
Mao, Y., Yang, Y., Ma, Z., Li, M., Su, H., & Zhang, J. (2020) · IEEE Access · doi:10.1109/access.2020.2985637 · Cited by 67
A Lightweight Feature Optimizing Network for Ship Detection in SAR Image
Zhang, X., Wang, H., Xu, C., Lv, Y., Fu, C., Xiao, H., & He, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2943241 · Cited by 67
Self-citation
Detection of Small Ship Objects Using Anchor Boxes Cluster and Feature Pyramid Network Model for SAR Imagery
Chen, P., Li, Y., Zhou, H., Liu, B., & Liu, P. (2020) · Journal of Marine Science and Engineering · doi:10.3390/jmse8020112 · Cited by 63
A High-Effective Implementation of Ship Detector for SAR Images
Gao, S., Liu, J., Miao, Y., & He, Z. J. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2021.3115121 · Cited by 58
Multitask Learning for Ship Detection From Synthetic Aperture Radar Images
Zhang, X., Huo, C., Xu, N., Jiang, H., Cao, Y., Ni, L., & Pan, C. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3102989 · Cited by 57
Self-citation
A Novel Detector Based on Convolution Neural Networks for Multiscale SAR Ship Detection in Complex Background
Dai, W., Mao, Y., Yuan, R., Liu, Y., Pu, X., & Li, C. (2020) · Sensors · doi:10.3390/s20092547 · Cited by 50
Multi-Scale Proposal Generation for Ship Detection in SAR Images
Liu, N., Cao, Z., Cui, Z., Pi, Y., & Dang, S. (2019) · Remote Sensing · doi:10.3390/rs11050526 · Cited by 39
Stochastic Gradient Descent
Shalev‐Shwartz, S., & Ben-David, S. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107298019.015 · Cited by 29
Multi-Scale SAR Ship Classification with Convolutional Neural Network
Xu, X., Zhang, X., & Zhang, T. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553116 · Cited by 21
Self-citation
Automatic moving targets detection using a rule-based system: comparison between different study cases
Ferrara, M. N., & Torre, A. (1998) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.1998.691633 · Cited by 11
SAR Ship Detection Under Complex Background Based on Attention Mechanism
Chen, C., Hu, C., He, C., Pei, H., Pang, Z., & Zhao, T. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9917-6_54 · Cited by 7
Ship Detection in Large-scale SAR Images Based on Dense Spatial Attention and Multi-level Feature Fusion
Zhang, L., Liu, Y., Guo, Q., Yin, H., Li, Y., & Du, P. (2021) · ACM Turing Celebration Conference - China (ACM TURC) · doi:10.1145/3472634.3472654 · Cited by 6
Small Vessel Detection Based on Adaptive Dual-Polarimetric Sar Feature Fusion and Attention-Enhanced Feature Pyramid Network
Zhang, F., Zhou, Y., Zhang, F., Yin, Q., & Ma, F. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9555096 · Cited by 3

Flagged first, then by citations

Research works printed but not linked: 4

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

Sorted by citations

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection
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-citation
Ship detection with deep learning: a survey
Er, M. J., Zhang, Y., Chen, J., & Gao, W. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10455-x · Cited by 137
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
Yasir, M., Jianhua, W., Xu, M., Sheng, H., Zeng, Z., Liu, S., Colak, A. T. I., & Hossain, M. S. (2022) · Soft Computing · doi:10.1007/s00500-022-07522-w · Cited by 123
Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5
Xu, B., Cui, X., Ji, W., Yuan, H., & Wang, J. (2023) · Agriculture · doi:10.3390/agriculture13010124 · Cited by 111
A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery
Zhou, Y., Liu, H., Ma, F., Pan, Z., & Zhang, F. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3264231 · Cited by 95
A Survey on Deep-Learning-Based Real-Time SAR Ship Detection
Li, J., Chen, J., Cheng, P., Yu, Z., Yu, L., & Chi, C. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3244616 · Cited by 90
YOLOShipTracker: Tracking ships in SAR images using lightweight YOLOv8
Yasir, M., Liu, S., Pirasteh, S., Xu, M., Sheng, H., Wan, J., de Figueiredo, F. A. P., Aguilar, F. J., & Li, J. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104137 · Cited by 90
Synthetic Aperture Radar for Geosciences
Meng, L., Yan, C., Lv, S., Sun, H., Xue, S., Li, Q., Zhou, L., Edwing, D., Edwing, K., Geng, X., et al. (2024) · Reviews of Geophysics · doi:10.1029/2023rg000821 · Cited by 89
A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection
Xu, X., Zhang, X., Shao, Z., Shi, J., Wei, S., Zhang, T., & Zeng, T. (2022) · Remote Sensing · doi:10.3390/rs14205276 · Cited by 72
Self-citation
Multi-scale ship target detection using SAR images based on improved Yolov5
Yasir, M., Liu, S., Xu, M., Sheng, H., Hossain, M. S., Colak, A. T. I., Wang, D., Jianhua, W., & Dang, K. B. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2022.1086140 · Cited by 69
Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task
Xie, F., Lin, B., & Liu, Y. (2022) · Sensors · doi:10.3390/s22093370 · Cited by 64
A Lightweight Model for Ship Detection and Recognition in Complex-Scene SAR Images
Xiong, B., Sun, Z., Wang, J., Leng, X., & Ji, K. (2022) · Remote Sensing · doi:10.3390/rs14236053 · Cited by 59
An efficient model for small object detection in the maritime environment
Shao, Z., Yin, Y., Lyu, H., Soares, C. G., Cheng, T., Jing, Q., & Yang, Z. (2024) · Applied Ocean Research · doi:10.1016/j.apor.2024.104194 · Cited by 58
AMFLW-YOLO: A Lightweight Network for Remote Sensing Image Detection Based on Attention Mechanism and Multiscale Feature Fusion
Peng, G., Yang, Z., Wang, S., & Yuan, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3327285 · Cited by 58
Maritime Ship Detection Method for Satellite Images Based on Multiscale Feature Fusion
Si, J., Song, B., Wu, J., Lin, W. S., Huang, W., & Chen, S. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3296898 · Cited by 54
Advanced ship detection and ocean monitoring with satellite imagery and deep learning for marine science applications
Bakırcı, M. (2024) · Regional Studies in Marine Science · doi:10.1016/j.rsma.2024.103975 · Cited by 54
TraCon: A Novel Dataset for Real-Time Traffic Cones Detection Using Deep Learning
Katsamenis, I., Karolou, E. E., Davradou, A., Protopapadakis, E. E., Doulamis, A. D., Doulamis, N. D., & Kalogeras, D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-17601-2_37 · Cited by 50
A Dual-Polarization Information-Guided Network for SAR Ship Classification
Shao, Z., Zhang, T., & Ke, X. (2023) · Remote Sensing · doi:10.3390/rs15082138 · Cited by 48
Self-citation
An improved target detection method based on YOLOv5 in natural orchard environments
Zhang, J., Tian, M., Yang, Z., Li, J., & Zhao, L. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108780 · Cited by 48
DiffSARShipInst: Diffusion model for ship instance segmentation from synthetic aperture radar imagery
Xu, X., Zhang, X., Wei, S., Shi, J., Zhang, W., Zhang, T., Zhan, X., Xu, Y., & Zeng, T. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.02.030 · Cited by 45
Self-citation
Semisupervised SAR Ship Detection Network via Scene Characteristic Learning
Du, Y., Du, L., Guo, Y., & Shi, Y. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3235859 · Cited by 44
YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach
Humayun, M. F., Nasir, F. A., Bhatti, F. A., Tahir, M., & Khurshid, K. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3365807 · Cited by 42
Real-time detection of road manhole covers with a deep learning model
Pang, D., Guan, Z., Luo, T., Su, W., & Dou, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-43173-z · Cited by 41
A Lightweight Convolutional Neural Network for Ship Target Detection in SAR Images
Hao, Y., & Zhang, Y. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3344396 · Cited by 40
RBFA-Net: A Rotated Balanced Feature-Aligned Network for Rotated SAR Ship Detection and Classification
Shao, Z., Zhang, X., Zhang, T., Xu, X., & Zeng, T. (2022) · Remote Sensing · doi:10.3390/rs14143345 · Cited by 40
Self-citation
SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection
Han, Y., Guo, J., Yang, H., Guan, R., & Zhang, T. (2024) · Drones · doi:10.3390/drones8040145 · Cited by 37
Self-citation
One-stage and lightweight CNN detection approach with attention: Application to WBC detection of microscopic images
Han, Z., Huang, H., Lu, D., Fan, Q., Ma, C., Chen, X., Gu, Q., & Chen, Q. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106606 · Cited by 37
MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes
Song, Y., Hong, S., Hu, C., He, P., Tao, L., Tie, Z., & Ding, C. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.038910 · Cited by 35
YOLOSAR-Lite: a lightweight framework for real-time ship detection in SAR imagery
Wang, H., Shi, J., Karimian, H., Liu, F., & Wang, F. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2405525 · Cited by 34
Gaussian meta-feature balanced aggregation for few-shot synthetic aperture radar target detection
Zhou, Z. O., Cui, Z., Tang, K., Tian, Y., Pi, Y., & Cao, Z. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.01.003 · Cited by 33
Ship detection based on multi-scale weighted fusion
Zhou, W., & Peng, Y. (2023) · Displays · doi:10.1016/j.displa.2023.102448 · Cited by 33
Lightweight SAR Ship Detection Network Based on Transformer and Feature Enhancement
Zhou, S., Zhang, M., Wu, L., Yu, D., Li, J., Fan, F., Zhang, L., & Liu, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3362954 · Cited by 32
Development and optimization of object detection technology in pavement engineering: A literature review
Yao, H., Fan, Y., Liu, Y., Cao, D., Chen, N., Luo, T., Yang, J., Hu, X., Ji, J., & You, Z. (2024) · Journal of Road Engineering · doi:10.1016/j.jreng.2024.01.006 · Cited by 34
Ship Detection With SAR C-Band Satellite Images: A Systematic Review
Alexandre, C., Devillers, R., Mouillot, D., Seguin, R., & Catry, T. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3437187 · Cited by 32
A Review of Deep-Learning-Based SAR Image Ship Interpretation Technology: The Latest Advances
Qiao, S., Zhang, Q., & Wang, Z. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3614504 · Cited by 32
High-Order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-Based Small Ship Detection
Yin, Y., Cheng, X., Shi, F., Liu, X., Huo, H., & Chen, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3349168 · Cited by 32
An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System
Yin, Y., Cheng, X., Shi, F., Zhao, M. Y., Li, G., & Chen, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3187454 · Cited by 31
YOLOv7oSAR: A Lightweight High-Precision Ship Detection Model for SAR Images Based on the YOLOv7 Algorithm
Liu, Y., Ma, Y., Chen, F., Shang, E., Yao, W., Zhang, S., & Yang, J. (2024) · Remote Sensing · doi:10.3390/rs16050913 · Cited by 30
Scale in Scale for SAR Ship Instance Segmentation
Shao, Z., Zhang, X., Wei, S., Shi, J., Ke, X., Xu, X., Zhan, X., Zhang, T., & Zeng, T. (2023) · Remote Sensing · doi:10.3390/rs15030629 · Cited by 30
Self-citation
EST-YOLOv5s: SAR Image Aircraft Target Detection Model Based on Improved YOLOv5s
Huang, M., Yan, W., Dai, W., & Wang, J. (2023) · IEEE Access · doi:10.1109/access.2023.3323575 · Cited by 30
LPST-Det: Local-Perception-Enhanced Swin Transformer for SAR Ship Detection
Yang, Z., Xia, X., Liu, Y., Wen, G., Zhang, W. E., & Guo, L. (2024) · Remote Sensing · doi:10.3390/rs16030483 · Cited by 29
AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11
He, R., Han, D., Shen, X., Han, B., Wu, Z., & Huang, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327362 · Cited by 30
An effective litchi detection method based on edge devices in a complex scene
Jiao, Z., Huang, K., Jia, G., Lei, H., Cai, Y., & Zhong, Z. (2022) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2022.07.009 · Cited by 29
A Lightweight Radar Ship Detection Framework with Hybrid Attentions
Yu, N., Ren, H., Deng, T., & Fan, X. (2023) · Remote Sensing · doi:10.3390/rs15112743 · Cited by 29
Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks
Cao, S., Zhao, C., Dong, J., & Fu, X. (2024) · Sensors · doi:10.3390/s24134290 · Cited by 28
LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion
Feng, K., Li, L., Wang, X., & Cui, X. (2023) · Remote Sensing · doi:10.3390/rs15225309 · Cited by 28
Ship detection and identification in SDGSAT-1 glimmer images based on the glimmer YOLO model
Song, W., Yan, D., Yan, J., Hu, C., Wu, W., & Wang, X. (2023) · International Journal of Digital Earth · doi:10.1080/17538947.2023.2277796 · Cited by 26
TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT
Qian, Y. J., & Wang, B. (2023) · IET Image Processing · doi:10.1049/ipr2.12991 · Cited by 26
Synthetic Aperture Radar (SAR) Meets Deep Learning
Zhang, T., Zeng, T., & Zhang, X. (2023) · Remote Sensing · doi:10.3390/rs15020303 · Cited by 26
Self-citation
Deep object segmentation and classification networks for building damage detection using the xBD dataset
Zhao, Z., Wang, F., Chen, S., Wang, H., & Cheng, G. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2302577 · Cited by 25
Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery
Guo, H., Bai, H., Yuan, Y., & Qin, W. (2022) · Remote Sensing · doi:10.3390/rs14081850 · Cited by 25
A Multiscale Feature Pyramid SAR Ship Detection Network With Robust Background Interference
Liu, S., Chen, P., & Zhang, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3325376 · Cited by 25
On the Effects of the Incidence Angle on the L-Band Multi-Polarisation Scattering of a Small Ship
Adil, M., Buono, A., Nunziata, F., Ferrentino, E., Velotto, D., & Migliaccio, M. (2022) · Remote Sensing · doi:10.3390/rs14225813 · Cited by 24
Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery
Liu, Y., Zhang, R., Deng, R., & Zhao, J. C. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2023.2196159 · Cited by 24
Doublem-net: multi-scale spatial pyramid pooling-fast and multi-path adaptive feature pyramid network for UAV detection
Li, Z., He, Q., Zhao, H., & Yang, W. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02278-1 · Cited by 23
MFFnet: Multimodal Feature Fusion Network for Synthetic Aperture Radar and Optical Image Land Cover Classification
Wang, Y., Zhang, W., Chen, W., Chen, C., & Liang, Z. (2024) · Remote Sensing · doi:10.3390/rs16132459 · Cited by 22
The Accelerated Inference of a Novel Optimized YOLOv5-LITE on Low-Power Devices for Railway Track Damage Detection
Dang, C., Wang, Z., He, Y., Wang, L., Cai, Y., Shi, H., & Jiang, J. (2023) · IEEE Access · doi:10.1109/access.2023.3334973 · Cited by 22
Infrared Maritime Small-Target Detection Based on Fusion Gray Gradient Clutter Suppression
Wang, W., Li, Z., & Siddique, A. (2024) · Remote Sensing · doi:10.3390/rs16071255 · Cited by 22
A Lightweight SAR Ship Detection Network Based on Deep Multiscale Grouped Convolution, Network Pruning, and Knowledge Distillation
Hu, B., & Miao, H. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3502172 · Cited by 22
A Lightweight Network for Ship Detection in SAR Images Based on Edge Feature Aware and Fusion
Li, Y., Liu, J., Li, X., Zhang, X., Wu, Z., & Han, B. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3524402 · Cited by 22
Self-citation
A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection
Pang, L., Li, B., Zhang, F., Meng, X., & Zhang, L. (2022) · Sensors · doi:10.3390/s22187088 · Cited by 22
Deep learning for ship detection in SAR images: A comprehensive review
Xu, X., Zhang, X., Wei, S., Shi, J., Li, J., Zhou, Z., Zhang, W., Tang, L., Sun, Z., Wang, Y., et al. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125045 · Cited by 21
Self-citation
Deep learning for efficient high-resolution image processing: A systematic review
Dede, A., Nunoo‐Mensah, H., Tchao, E. T., Agbemenu, A. S., Adjei, P. E., Acheampong, F. A., & Kponyo, J. J. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200505 · Cited by 21
Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance
Cheng, J., Xiang, D., Tang, J., Zheng, Y., Guan, D., & Du, B. (2022) · Remote Sensing · doi:10.3390/rs14122832 · Cited by 20
Context-aware SAR image ship detection and recognition network
Li, C., Yue, C., Li, H., & Wang, Z. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1293992 · Cited by 20
YOLOSeaShip: a lightweight model for real-time ship detection
Jiang, X., Cai, J., & Wang, B. (2024) · European Journal of Remote Sensing · doi:10.1080/22797254.2024.2307613 · Cited by 20
A Lightweight YOLO-Based Model in Small-Object Detection for AAV Optical Sensors
Sun, H., Shi, B., & Hu, Y.-L. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3553871 · Cited by 19
Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images
Chen, B., Lv, Z., Lu, P., Shu, G., Huang, Y., & Li, N. (2022) · Remote Sensing · doi:10.3390/rs14174294 · Cited by 19
Real time road defect monitoring from UAV visual data sources
Katsamenis, I., Bakalos, N., Protopapadakis, E. E., Karolou, E. E., Kopsiaftis, G., & Voulodimos, A. S. (2023) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3594806.3596561 · Cited by 19
WDFA-YOLOX: A Wavelet-Driven and Feature-Enhanced Attention YOLOX Network for Ship Detection in SAR Images
Wu, F., Hu, T., Yu, X., Ma, B., Sarwar, S., & Zhang, C. (2024) · Remote Sensing · doi:10.3390/rs16101760 · Cited by 19
A one-stage deep learning based method for automatic analysis of droplet-based digital PCR images
Yao, Y., Zhao, S., Liang, Y., Hu, F., & Peng, N. (2023) · The Analyst · doi:10.1039/d3an00615h · Cited by 18
SA$^{2}$Net: Ship Augmented Attention Network for Ship Recognition in SAR Images
Shang, Y., Pu, W., Liao, D., Ji, Y., Wu, C., Huang, Y., Zhang, Y., Wu, J., Yang, J., & Wu, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3317489 · Cited by 18
Effective Vessel Recognition in High Resolution SAR Images Using Quantitative and Qualitative Training Data Enhancement From Target Velocity Phase Refocusing
Song, J., Kim, D., Hwang, J.-H., Kim, H., Li, C., Han, S., & Kim, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3346171 · Cited by 18
Terminal Congestion Analysis of Container Ports Using Satellite Images and AIS
Yasuda, K., Shibasaki, R., Yasuda, R., & Murata, H. (2024) · Remote Sensing · doi:10.3390/rs16061082 · Cited by 18
Classification and Counting of Ships Using YOLOv5 Algorithm
Suliva, R. S. S., Valencia, C. A. A., & Villaverde, J. F. (2022) · International Conference on Communication and Information Systems (ICCIS) · doi:10.1109/iccis56375.2022.9998129 · Cited by 17
Ship-Yolo: A Deep Learning Approach for Ship Detection in Remote Sensing Images
Shi, W., Zheng, W., & Xu, Z. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13040737 · Cited by 17
On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images
Shen, Y., Liu, D., Chen, J., Wang, Z., Wang, Z., & Zhang, Q. (2023) · Remote Sensing · doi:10.3390/rs15163963 · Cited by 17
Multi‐granularity feature enhancement network for maritime ship detection
Ying, L., Miao, D., Zhang, Z., Zhang, H., & Witold Pedrycz, C. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12310 · Cited by 16
PTCDet: advanced UAV imagery target detection
Su, J., Qin, Y., Jia, Z., & Hou, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-78749-w · Cited by 16
DWSC-YOLO: A Lightweight Ship Detector of SAR Images Based on Deep Learning
Jiang, S.-X., & Zhou, X. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10111699 · Cited by 16
STD-Yolov5: a ship-type detection model based on improved Yolov5
Ning, Y., Zhao, L., Zhang, C., & Yuan, Z. (2022) · Ships and Offshore Structures · doi:10.1080/17445302.2022.2142362 · Cited by 15
PPDM-YOLO: A Lightweight Algorithm for SAR Ship Image Target Detection in Complex Environments
He, H., Hu, T., Xu, S., Xu, H., Song, L., & Sun, Z. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3602497 · Cited by 14
A Deep-Learning-Based Lightweight Model for Ship Localizations in SAR Images
Bhattacharjee, S., Shanmugam, P., & Das, S. (2023) · IEEE Access · doi:10.1109/access.2023.3310539 · Cited by 14
Fine-grained ship image classification and detection based on a vision transformer and multi-grain feature vector FPN model
Wang, F., Yu, D., Huang, L., Zhang, Y., Chen, Y., & Wang, Z. (2024) · Geo-spatial Information Science · doi:10.1080/10095020.2024.2331552 · Cited by 14
Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks
Sousa, K. D., Pilikos, G., Azcueta, M., & Floury, N. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3399021 · Cited by 14
Infrared remote sensing ship image object detection model based on YOLO In multiple environments
Ge, Y., Ji, H., & Liu, X. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03656-6 · Cited by 14
HE-YOLOv5s: Efficient Road Defect Detection Network
Liu, Y., Duan, M., Ding, G., Ding, H., Peng, H., & Zhao, H. (2023) · Entropy · doi:10.3390/e25091280 · Cited by 14
Edge-Constrained Guided Feature Perception Network for Ship Detection in SAR Images
Xu, S., Fan, J., Jia, X., & Chang, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3317060 · Cited by 14
SegS-YOLO: A YOLO-Based Instance Segmentation Approach for Small-Scale Ship Targets in SAR Images
Zhang, Y., Sun, Z., Chang, S., Tang, B., & Hou, B. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3635282 · Cited by 13
Filling and Disentanglement: Toward Low- and High-Order Parallel Single-Domain Generalization for SAR Ship Detection
Yuan, Y., Tang, L., Xu, Y., Lin, C., Chen, C., Huang, Y., & Ding, X. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3489572 · Cited by 13
Scale-aware dimension-wise attention network for small ship instance segmentation in synthetic aperture radar images
Ke, X., Zhang, T., & Shao, Z. (2023) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.17.046504 · Cited by 13
Self-citation
Ship Target Detection in SAR Images Based on Multiple Attention Mechanism and Cross-Scale Feature Fusion
Wang, Y., Wu, T., Guo, L., & Mo, Y. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3582861 · Cited by 11
SSGY: A Lightweight Neural Network Method for SAR Ship Detection
He, F.-S., Wang, C., & Guo, B. (2025) · Remote Sensing · doi:10.3390/rs17162868 · Cited by 11
Small Object Detection Methods in Complex Background: An Overview
Li, Z., Guo, Q., Sun, B., Cao, D., Li, Y., & Sun, X. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001423500027 · Cited by 11
KDSMALL: A lightweight small object detection algorithm based on knowledge distillation
Wen, Z., Wang, X., Fan, Y., Yang, Y., Wen, Y., Li, Y., Xu, Y., Lin, Z., Chen, L., Yao, S., et al. (2024) · Computer Communications · doi:10.1016/j.comcom.2023.12.018 · Cited by 11
EMO-YOLO: a lightweight ship detection model for SAR images based on YOLOv5s
Pan, H., Guan, S., & Jia, W. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03258-2 · Cited by 11
A Lightweight Self-Supervised Representation Learning Algorithm for Scene Classification in Spaceborne SAR and Optical Images
Xiao, X., Li, C., & Lei, Y. (2022) · Remote Sensing · doi:10.3390/rs14132956 · Cited by 11
Lightweight model for small target detection of SAR images of ships based on NWD loss
Yan, C., & Liu, C. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03420-w · Cited by 10

Lightweight One-Stage Ship Detection Based on SAR Images for Embedded Platform
Lu, C. W., Shen, J., Zhou, S., Zheng, Y., & Yuan, J. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-7188-5_7
YOLO-Lite: An Efficient Lightweight Network for SAR Ship Detection
Ren, X., Bai, Y., Liu, G., & Zhang, P. (2023) · Remote Sensing · doi:10.3390/rs15153771 · Cited by 91
A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection
Pang, L., Li, B., Zhang, F., Meng, X., & Zhang, L. (2022) · Sensors · doi:10.3390/s22187088 · Cited by 22
A Super Lightweight and Efficient SAR Image Ship Detector
Yang, Y., Ju, Y., & Zhou, Z. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3284093 · Cited by 28
On-Board Ship Detection in SAR Images Based on L-YOLO
Xu, X., Zhang, X., Zhang, T., Shi, J., Wei, S., & Li, J. (2022) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf2248738.2022.9764190 · Cited by 14
Self-citation

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks