Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (4): 712-723.doi: 10.11947/j.AGCS.2024.20220605
• Photogrammetry and Remote Sensing • Previous Articles Next Articles
Qihao HUANG1(), Guowang JIN1(), Xin XIONG1, Limei WANG2, Jiahao LI1
Received:
2022-10-24
Revised:
2023-11-08
Published:
2024-05-13
Contact:
Guowang JIN
E-mail:hqh_rs@163.com;Guowang_jin@163.com
About author:
HUANG Qihao (1998—), male, postgraduate, majors in synthetic aperture radar target detection and recognition. E-mail: hqh_rs@163.com
Supported by:
CLC Number:
Qihao HUANG, Guowang JIN, Xin XIONG, Limei WANG, Jiahao LI. Lightweight SAR target detection based on channel pruning and know-ledge distillation[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(4): 712-723.
Tab. 3
Results of teacher networks with different pruning ratios improving student network performance"
学生网络 | 教师网络 | 剪枝比例/(%) | 平均准确率/(%) | 平均召回率/(%) | 平均精度/(%) | 模型体积/MB | 推理时间/ms |
---|---|---|---|---|---|---|---|
YOLOv5-n | × | × | 91.4 | 91.6 | 92.2 | 3.73 | 1.7 |
YOLOv5-n | YOLOv5-m | × | 92.1 | 90.6 | 94.1 | ||
10 | 91.6 | 92.5 | 94.7 | ||||
20 | 91.4 | 93.8 | 94.6 | ||||
30 | 91.9 | 92.9 | 95.0 | ||||
40 | 92.0 | 92.6 | 94.9 | ||||
50 | 90.2 | 93.6 | 93.8 | ||||
60 | 91.1 | 92.6 | 93.9 | ||||
69 | 90.9 | 93.4 | 94.1 |
Tab. 4
Optimization results of different pruning ratios on teacher network performance"
网络模型 | 剪枝比例/(%) | 平均准确率/(%) | 平均召回率/(%) | 平均精度/(%) | 模型体积/MB | 推理时间/ms |
---|---|---|---|---|---|---|
YOLOv5-m(基准) | — | 91.4 | 92.5 | 94.2 | 40.2 | 8.9 |
教师网络A | 10 | 91.1 | 93.9 | 94.3 | 36.2 | 6.9 |
教师网络B | 20 | 91.2 | 94.5 | 94.9 | 31.1 | 6.4 |
教师网络C | 30 | 91.6 | 94.6 | 94.9 | 26.6 | 6.2 |
教师网络D | 40 | 91.0 | 93.7 | 94.5 | 22.6 | 6.0 |
教师网络E | 50 | 92.2 | 94.4 | 95.3 | 19.2 | 5.8 |
教师网络F | 60 | 90.5 | 94.4 | 94.6 | 16.5 | 5.5 |
教师网络G | 69 | 91.0 | 94.9 | 94.7 | 14.8 | 5.5 |
Tab. 5
Results of performance comparison of different SAR target detection algorithms"
方法 | 平均准确率/(%) | 平均召回率/(%) | 平均精度/(%) | 模型体积/MB | 推理时间/ms |
---|---|---|---|---|---|
YOLOv3 | 90.8 | 92.0 | 93.2 | 117.00 | 12.8 |
YOLOv3-tiny | 90.9 | 75.1 | 84.1 | 16.60 | 1.9 |
YOLOv7 | 93.0 | 93.2 | 94.1 | 71.30 | 11.5 |
YOLOv7-tiny | 91.1 | 88.6 | 89.6 | 11.70 | 3.4 |
YOLOX-s | 92.2 | 93.8 | 94.9 | 34.30 | 7.9 |
YOLOX-tiny | 91.5 | 92.7 | 93.0 | 19.40 | 4.8 |
YOLOX-nano | 90.7 | 92.1 | 92.5 | 3.60 | 1.5 |
YOLOv5-n | 91.4 | 91.6 | 92.2 | 3.73 | 1.7 |
本文方法(30%通道剪枝+知识蒸馏) | 91.9 | 92.9 | 95.0 | 3.73 | 1.7 |
Tab. 6
Generalization test results statistics and methods comparison in different regions"
区域 | 场景类型 | 目标类型 | 知识蒸馏方法 | 本文方法 | ||||
---|---|---|---|---|---|---|---|---|
平均准确率/(%) | 平均召回率/(%) | F1值 | 平均准确率/(%) | 平均召回率/(%) | F1值 | |||
A | 城区 | 桥梁 | 100.00 | 50.00 | 0.666 7 | 100.00 | 62.50 | 0.769 2 |
B | 码头 | 靠岸舰船 | 69.23 | 60.00 | 0.642 9 | 90.91 | 66.67 | 0.769 3 |
C | 远海 | 舰船 | 100.00 | 90.91 | 0.952 4 | 100.00 | 100.00 | 1.000 0 |
D | 突堤式码头 | 靠岸舰船、油罐 | 90.99 | 90.18 | 0.905 8 | 95.96 | 84.82 | 0.900 5 |
E | 远海 | 舰船 | 100.00 | 50.00 | 0.666 7 | 100.00 | 83.33 | 0.909 1 |
F | 货场码头 | 靠岸舰船、桥梁 | 72.73 | 66.67 | 0.695 7 | 87.50 | 58.33 | 0.700 0 |
平均值 | 88.83 | 67.96 | 0.755 0 | 95.73 | 77.43 | 0.841 3 |
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