SAR Ship Detection Algorithm Based on Deep Dense Sim Attention Mechanism Network

Shan, Huilin; Fu, Xiangwei; Lv, Zongkui; Zhang, Yinsheng

doi:10.1109/jsen.2023.3284959

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…Improved YOLO models were proposed in [36][37][38] to achieve model compression while improving accuracy. A lightweight backbone network based on deep dense sim attention mechanism network (DDSA-Net) was introduced in [39]. Results demonstrate that the proposed algorithm performs well in terms of speed and accuracy and has better robustness and real-time performance compared to similar detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Wang,

Hou,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) can operate around the clock and in all weather, and therefore high-resolution SAR images have been frequently applied for ship inspection. However, current ship target detection and identification methods have limited detection accuracy and lead to missing detection of small targets due to speckle noise caused by the imaging principle of SAR imagery and complex nearshore interference. Therefore, this paper proposes an improved YOLOv5 method to address the problem of low accuracy in multi-ship target detection tasks in complex scenes. The developed scheme enhances the ship target detection performance while reducing the number of parameters. Specifically, first, we improve the size of the input SAR images and optimize the anchor frames of ship targets in the training dataset to locate small target ships more accurately. Then, symmetric pyramidal non-local block(APNB) and Sim attention mechanism (SimAM) are introduced to reduce near-shore background interference. Additionally, to make the C3 module output richer and with more feature information, channel shuffling is performed after the C3 output to enhance the information exchange between channels. Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the NECK part is replaced with Ghost convolution. The index F1 of the proposed method on the High-Resolution SAR Image Dataset (HRSID) and SAR Ship Detection Dataset (SSDD) reaches the highest of 91.3% and 95.8%, respectively. MAPs (0.5:0.95) for the two datasets are both the highest, which are at least 2% higher than the suboptimal method. In selected specific inshore scenes, the ship detection performance of the proposed method outperforms current advanced methods for multiscale ships. It is shown that the proposed method can extract ship features effectively in complex scenes and its effectiveness is further validated on the large scene AIR-SARShip-1 dataset.

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