Opto-Electronic Engineering, Volume. 52, Issue 2, 240254-1(2025)
Multi-granularity feature and shape-position similarity metric method for ship detection in SAR images
Figures & Tables(21)
Fig. 1. Diagram of the overall network structure of the proposed method
Fig. 5. Changes in receptive field of the model before and after using HWT. Receptive fields of (a) Stage2, (b) Stage3, (c) Stage4, and (d) Stage5 before HWT; Receptive fields of (e) Stage2, (f) Stage3, (g) Stage4, and (h) Stage5 after HWT
Fig. 6. IoU changes of ships with different sizes. (a) Changes in IoU for small ship; (b) Changes in IoU for large ship
Fig. 7. Simulation comparison of different metrics under different deviations. (a) Deviation; (b) IoU deviation curves; (c) SPS deviation curves
Fig. 8. SSDD and HRSID ship target distributions. (a) SSDD; (b) HRSID
Fig. 9. Changes in regression loss, accuracy, and recall rates of the model before and after using SPS. (a) Regression loss; (b) Precision; (c) Recall
Fig. 12. Visual comparison of different methods on SSDD dataset. (a) True labeling; (b) Dynamic R-cnn; (c) YOLOv8n; (d) YOLO11n; (e) Mamba YOLO; (f) Ours; (g) DINO; (h) RT-DETR
Fig. 13. Visual comparison of different methods on HRSID dataset. (a) True labeling; (b) Dynamic R-CNN; (c) YOLOv8n; (d) YOLO11n; (e) Mamba YOLO; (f) Ours; (g) DINO; (h) RT-DETR
Table 1. Influence of different configurations in HWT on model performance
View tableView in ArticleTable 1. Influence of different configurations in HWT on model performance
Level Kernel size RFS mAP/% mAP50/% Params/k FLOPs/M 1 3 × 3 6 67.0 95.8 2.9 7.4 5 × 5 10 67.4 96.3 8.1 20.5 7 × 7 14 67.8 96.9 15.7 40.1 2 3 × 3 12 67.6 96.8 5.3 8.3 5 × 5 20 68.1 97.8 14.5 23.0 7 × 7 28 68.1 97.9 28.3 45.2 3 3 × 3 24 67.9 97.4 7.6 8.5 5 × 5 40 67.6 97.1 20.9 23.7 7 × 7 56 67.3 97.0 40.8 46.4
Table 2. Results of MGFA ablation experiments
View tableView in ArticleTable 2. Results of MGFA ablation experiments
Coarse-grained feature Fine-grained feature mAP/% mAP50/% √ － 67.6 97.5 － √ 66.9 95.8 √ √ 68.1 97.8
Table 3. Effect of weighting coefficients on model performance
View tableView in ArticleTable 3. Effect of weighting coefficients on model performance
α 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 mAP/% 67.6 67.5 67.7 67.8 67.8 67.7 67.3 67.2 66.9 66.9 66.8 mAP50/% 96.7 96.7 97.1 97.3 97.1 96.9 96.4 96.4 96.0 95.8 95.6
Table 4. Results of ablation experiments
View tableView in ArticleTable 4. Results of ablation experiments
MGFA SPS mAP/% mAP50/% － － 67.2 96.1 √ － 68.1 97.8 － √ 67.8 97.3 √ √ 68.8 98.3
Table 5. Comparison of different metrics on SSDD dataset
View tableView in ArticleTable 5. Comparison of different metrics on SSDD dataset
Metric CIoU GIoU DIoU EIoU SIoU MPDIoU Powerful-IoU SPS mAP/% 67.2 67.2 67.4 66.8 67.1 67.6 67.5 67.8 mAP50/% 96.1 96.0 96.6 96.4 96.1 96.6 96.8 97.3
Table 6. Comparison of different methods on SSDD dataset
View tableView in ArticleTable 6. Comparison of different methods on SSDD dataset
Method mAP/% mAP50/% mAPs/% mAPm/% mAPl/% Params/M FLOPs/G Two stage Faster R-CNN 60.7 92.6 55.4 70.1 69.0 41.4 103.2 Dynamic R-CNN 66.9 96.1 65.1 74.3 66.2 41.3 107.3 Sparse R-CNN 54.4 91.2 50.2 62.1 60.3 106.0 77.5 One stage TOOD 67.1 96.7 62.8 75.0 83.7 32.0 95.3 RTMDet 68.0 97.7 66.5 78.7 74.5 4.9 8.0 YOLOv8n 68.3 97.5 65.8 75.5 62.9 3.2 8.7 YOLO11n 67.2 96.1 64.5 72.0 60.7 2.6 6.5 Mamba YOLO 68.6 97.1 65.1 74.4 68.4 6.1 14.3 Ours 68.8 98.3 66.8 72.5 63.1 2.4 6.4 DETR like DDQ 67.1 96.4 58.1 72.0 80.1 48.3 123.3 DINO 69.5 98.1 63.2 75.6 85.0 47.5 139.0 RT-DETR 64.2 94.0 59.7 69.7 57.0 36.0 100.2
Table 7. Comparison of different metrics on HRSID dataset
View tableView in ArticleTable 7. Comparison of different metrics on HRSID dataset
Metric CIoU GIoU DIoU EIoU SIoU MPDIoU Powerful-IoU SPS mAP/% 68.5 68.7 68.9 66.6 68.1 68.6 69.3 69.6 mAP50/% 91.3 92.0 91.8 91.0 91.1 91.1 92.2 92.8
Table 8. Comparison of different methods on HRSID dataset
View tableView in ArticleTable 8. Comparison of different methods on HRSID dataset
Method mAP/% mAP50/% mAPs/% mAPm/% mAPl/% Params/M FLOPs/G Two stage Faster R-CNN 63.5 85.4 48.8 78.7 49.9 41.4 103.2 Dynamic R-CNN 63.8 85.1 48.7 78.2 55.7 41.3 107.3 Sparse R-CNN 61.0 83.4 46.4 75.2 46.6 106.0 77.5 One stage TOOD 70.3 92.0 58.1 82.2 67.6 32.0 95.3 RTMDet 69.0 90.5 55.9 81.3 63.7 4.9 8.0 YOLOv8n 69.4 93.6 57.8 79.7 57.6 3.2 8.7 YOLO11n 68.5 91.3 57.4 78.5 53.2 2.6 6.5 Mamba YOLO 70.4 93.7 58.6 80.8 67.7 6.1 14.3 Ours 70.8 93.8 60.2 79.3 57.9 2.4 6.4 DETR like DDQ 70.5 93.4 59.4 81.1 71.3 48.3 123.3 DINO 69.7 92.5 59.3 79.9 62.7 47.5 139.0 RT-DETR 61.9 89.5 48.1 75.0 42.6 36.0 100.2
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Shibo Li, Zhenjiu Xiao, Haicheng Qu, Fukun Li, Jingjing Wang. Multi-granularity feature and shape-position similarity metric method for ship detection in SAR images[J]. Opto-Electronic Engineering, 2025, 52(2): 240254-1
Paper Information
Category: Article
Received: Oct. 30, 2024
Accepted: Dec. 17, 2024
Published Online: Apr. 27, 2025
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