LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism
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| Udgivet i: | Remote Sensing vol. 17, no. 14 (2025), p. 2514-2542 |
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| Hovedforfatter: | |
| Andre forfattere: | , , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Zhao, Yuliang |u Aulin College, Northeast Forestry University, Harbin 150040, China; zhaoyuliang@nefu.edu.cn (Y.Z.); qiutongwang@nefu.edu.cn (Q.W.); lch@nefu.edu.cn (C.L.) | |
| 245 | 1 | |a LWSARDet: A Lightweight SAR Small Ship Target Detection Network Based on a Position–Morphology Matching Mechanism | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Image resolution | ||
| 653 | |a Morphology | ||
| 653 | |a Edge computing | ||
| 653 | |a Fines & penalties | ||
| 653 | |a Computer applications | ||
| 653 | |a Linear transformations | ||
| 653 | |a Localization | ||
| 653 | |a Efficiency | ||
| 653 | |a Redundancy | ||
| 653 | |a Datasets | ||
| 653 | |a Remote sensing | ||
| 653 | |a Matching | ||
| 653 | |a Noise reduction | ||
| 653 | |a Synthetic aperture radar | ||
| 653 | |a Ground truth | ||
| 653 | |a Dilution | ||
| 653 | |a Neural networks | ||
| 653 | |a Target detection | ||
| 653 | |a Computing costs | ||
| 653 | |a Design | ||
| 653 | |a False alarms | ||
| 653 | |a Algorithms | ||
| 653 | |a Complexity | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Du, Yang |u Computer and Control Engineering College, Northeast Forestry University, Harbin 150040, China; miaoyan@nefu.edu.cn | |
| 700 | 1 | |a Wang Qiutong |u Aulin College, Northeast Forestry University, Harbin 150040, China; zhaoyuliang@nefu.edu.cn (Y.Z.); qiutongwang@nefu.edu.cn (Q.W.); lch@nefu.edu.cn (C.L.) | |
| 700 | 1 | |a Li, Changhe |u Aulin College, Northeast Forestry University, Harbin 150040, China; zhaoyuliang@nefu.edu.cn (Y.Z.); qiutongwang@nefu.edu.cn (Q.W.); lch@nefu.edu.cn (C.L.) | |
| 700 | 1 | |a Miao, Yan |u Computer and Control Engineering College, Northeast Forestry University, Harbin 150040, China; miaoyan@nefu.edu.cn | |
| 700 | 1 | |a Wang, Tengfei |u China Railway Tunnel Group Co., Ltd., Changchun 130022, China; wangtengff_tt@163.com | |
| 700 | 1 | |a Song, Xiangyu |u Civil Engineering College, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; songxiangyu@stdu.edu.cn | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 14 (2025), p. 2514-2542 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233250677/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233250677/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |