通道剪枝与知识蒸馏相结合的轻量化SAR目标检测

doi:10.11947/j.AGCS.2024.20220605

测绘学报 ›› 2024, Vol. 53 ›› Issue (4): 712-723.doi: 10.11947/j.AGCS.2024.20220605

通道剪枝与知识蒸馏相结合的轻量化SAR目标检测

黄启灏¹(), 靳国旺¹(), 熊新¹, 王丽美², 李佳豪¹

^1.信息工程大学地理空间信息学院，河南　郑州　450001
^2.河南城建学院测绘与城市空间信息学院，河南　平顶山　467000

收稿日期:2022-10-24 修回日期:2023-11-08 发布日期:2024-05-13
通讯作者: 靳国旺 E-mail:hqh_rs@163.com;Guowang_jin@163.com
作者简介:黄启灏（1998—），男，硕士生，研究方向为合成孔径雷达目标检测与识别。E-mail：hqh_rs@163.com
基金资助:
国家自然科学基金(41474010)

Lightweight SAR target detection based on channel pruning and know-ledge distillation

Qihao HUANG¹(), Guowang JIN¹(), Xin XIONG¹, Limei WANG², Jiahao LI¹

^1.Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
^2.School of Surveying and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467000, China

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:
The National Natural Science Foundation of China(41474010)

摘要/Abstract

摘要：

轻量化SAR目标检测方法对快速检测SAR影像中的地物目标具有重要意义。针对轻量化检测方法精度不高的问题，设计了一种通道剪枝与知识蒸馏相结合的轻量化SAR目标检测方法。该方法通过对复杂网络中批归一化层的缩放因子γ进行稀疏化训练，判别对应特征通道的重要程度，进而裁剪次要通道，并在微调训练后将其作为教师网络，构造知识蒸馏框架指导轻量模型训练，提高轻量模型的检测精度。采用YOLOv5-6.1算法搭建了检测框架，并在重组的MSAR和SSDD多类目标数据集上进行了训练和检测试验，结果表明该方法能够在保持模型体积仅3.73 MB的轻量化条件下，提升SAR目标检测精度，验证了本文方法的有效性。

关键词: SAR, 目标检测, 轻量化, 通道剪枝, 知识蒸馏

Abstract:

Lightweight SAR target detection algorithm is of great significance for the rapid detection of ground objects in SAR images. Aiming at the low precision of lightweight detection algorithm, a lightweight SAR target detection method combining channel pruning and knowledge distillation was designed. In this method, the importance of the corresponding feature channels is identified by sparse training of the scaling factor γ of the batch normalization layer in the complex network, and then the secondary channels are cut. After fine-tuning training, it is used as a teacher network to construct a knowledge distillation framework to guide the training of lightweight model and improve the detection accuracy of light weight model. The YOLOv5-6.1 algorithm was used to build a detection framework, and the training and detection experiments were carried out on the reconstituted MSAR and SSDD multi-class target datasets. The results show that the proposed method can improve the accuracy of SAR target detection while maintaining the lightweight model size of only 3.73 MB, which verifies the effectiveness of the proposed method.

Key words: SAR, target detection, lightweight, channel pruning, knowledge distillation

中图分类号:

P237

黄启灏, 靳国旺, 熊新, 王丽美, 李佳豪. 通道剪枝与知识蒸馏相结合的轻量化SAR目标检测[J]. 测绘学报, 2024, 53(4): 712-723.

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.

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目标类型	数据集	训练集/张	验证集/张	平均目标数/(个/张)
油罐	MSAR	1000	250	9.86
桥梁	MSAR	1266	316	1.17
舰船	SSDD	812	232	2.12

网络模型	平均准确率/(%)	平均召回率/(%)	平均精度/(%)	模型体积/MB	平均推理时间/ms
YOLOv5-x	90.9	93.6	93.9	165.00	23.9
YOLOv5-l	90.9	90.9	93.1	88.50	14.4
YOLOv5-m	91.4	92.5	94.2	40.20	8.9
YOLOv5-s	91.3	93.3	94.0	13.70	3.6
YOLOv5-n	91.4	91.6	92.2	3.73	1.7

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

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

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

通道剪枝与知识蒸馏相结合的轻量化SAR目标检测

Lightweight SAR target detection based on channel pruning and know-ledge distillation

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摘要/Abstract

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图/表 15

参考文献 28

相关文章 15

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Metrics

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区域	场景类型	目标类型	知识蒸馏方法			本文方法
区域	场景类型	目标类型	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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