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LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism

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Publié dans:Remote Sensing vol. 17, no. 14 (2025), p. 2514-2542
Auteur principal: Zhao, Yuliang
Autres auteurs: Du, Yang, Wang Qiutong, Li, Changhe, Miao, Yan, Wang, Tengfei, Song, Xiangyu
Publié:
MDPI AG
Sujets:
Feature extraction
Accuracy
Image resolution
Morphology
Edge computing
Fines & penalties
Computer applications
Linear transformations
Localization
Efficiency
Redundancy
Datasets
Remote sensing
Matching
Noise reduction
Synthetic aperture radar
Ground truth
Dilution
Neural networks
Target detection
Computing costs
Design
False alarms
Algorithms
Complexity
Semantics
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Résumé:The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods.
ISSN:2072-4292
DOI:10.3390/rs17142514
Source:Advanced Technologies & Aerospace Database