Chinese Journal of Ship Research, Volume. 19, Issue 5, 188(2024)
Lightweight and robust ship detection method driven by self-attention mechanism
Figures & Tables(15)
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Table 1. Initial parameters of the model
View tableView in ArticleTable 1. Initial parameters of the model
参数 数值 学习率 0.01 余弦退火超参数 0.1 动量 0.937 权重衰退 0.000 5 批量大小 16 输入尺寸 640 优化器 SGD
Table 2. Comparison results of evaluation indicators for algorithm models
View tableView in ArticleTable 2. Comparison results of evaluation indicators for algorithm models
算法模型 平均精度 平均召回率 mAP@0.5 mAP@0.5:0.95 参数量 YOLOv3 0.877 0.901 0.915 0.615 61 513 575 YOLOv5 0.919 0.868 0.916 0.618 46 124 433 YOLOv7 0.905 0.869 0.926 0.620 36 497 954 Faster R-CNN 0.538 0.331 0.533 0.268 138 357 544 SSD 0.643 0.558 0.521 0.281 48 306 395 ShipDet 0.921 0.903 0.929 0.629 35 366 310
Table 3. Experimental results of different types of ships
View tableView in ArticleTable 3. Experimental results of different types of ships
算法模型 mAP@0.5:0.95 普通船 集装箱船 拖轮 客船 全类 YOLOv3 0.523 0.602 0.715 0.62 0.615 YOLOv5 0.527 0.600 0.718 0.627 0.618 YOLOv7 0.508 0.631 0.719 0.621 0.620 Faster R-CNN 0.129 0.403 0.255 0.285 0.268 SSD 0.143 0.399 0.240 0.342 0.281 ShipDet 0.546 0.618 0.719 0.623 0.629
Table 4. Comparison results of different models on different scales
View tableView in ArticleTable 4. Comparison results of different models on different scales
算法模型 平均精度(mAP@0.5:0.95) 平均召回率( mAP@0.5:0.95) 小尺寸船舶 中尺寸船舶 大尺寸船舶 小尺寸船舶 中尺寸船舶 大尺寸船舶 YOLOv3 0.389 0.664 0.763 0.474 0.729 0.806 YOLOv5 0.410 0.671 0.785 0.483 0.729 0.827 YOLOv7 0.421 0.683 0.770 0.501 0.754 0.813 Faster R-CNN 0.004 0.252 0.551 0.009 0.378 0.600 SSD 0.142 0.355 0.593 0.102 0.411 0.616 ShipDet 0.457 0.677 0.767 0.503 0.739 0.808
Table 5. Results of ablation experiments
View tableView in ArticleTable 5. Results of ablation experiments
模型 SIoU损失 函数 STR 模块 平均 精度 平均 召回率 mAP@0.5 mAP@0.5:0.95 YOLOv5L × × 0.903 0.868 0.916 0.618 SD-NET √ × 0.898 0.902 0.919 0.618 × √ 0.911 0.893 0.918 0.621 √ √ 0.921 0.903 0.929 0.629
Table 6. Detection results based on Seaships dataset with different algorithms
View tableView in ArticleTable 6. Detection results based on Seaships dataset with different algorithms
算法模型 平均精度 平均召回率 mAP@0.5 mAP@0.5:0.95 YOLOv3 0.980 0.991 0.993 0.829 YOLOv5 0.985 0.976 0.923 0.832 YOLOv7 0.976 0.977 0.991 0.836 Faster R-CNN 0.937 0.959 0.900 0.786 SSD 0.887 0.890 0.892 0.564 ShipDet 0.990 0.991 0.993 0.899
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Feng MA, Zihui SHI, Jie SUN, Chen CHEN, Xianbin MAO, Xinping YAN. Lightweight and robust ship detection method driven by self-attention mechanism[J]. Chinese Journal of Ship Research, 2024, 19(5): 188
Paper Information
Category: Weapon, Electronic and Information System
Received: May. 30, 2023
Accepted: --
Published Online: Mar. 14, 2025
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