基于动态聚合网络的SAR目标检测

doi:10.12305/j.issn.1001-506X.2025.08.11

Abstract

Abstract:

Due to the imaging mechanism of synthetic aperture radar (SAR) images, SAR images are severely affected by noise and there is an imbalance in SAR target categories, making it difficult to characterize SAR target features. In view of these problems, a SAR target detection method based on dynamic aggregation network (DANet) is proposed. This network is based on the fully convolutional one-stage object detection (FCOS) network without anchor boxes. DANet embeds a dynamic aggregation module with dynamic coordinate attention in the feature pyramid network to improve the SAR target feature learning ability under noise influence. In order to alleviate the problem of imbalanced numbers between different categories in SAR datasets, DANet introduces a class balanced dynamic intersection over union (IoU) loss function in the regression branch of the detection head, which guides model parameter updates through dynamic non monotonic mechanisms and class balance factors. The experimental results on the MSAR-1.0 dataset and SAR-AIRcraft-1.0 dataset show that DANet achieved target detection accuracies of 70.25% and 52.36%, respectively, on the aforementioned SAR target detection datasets. Compared with the benchmark network FCOS, its average accuracy increased by 2.73% and 3.67%, respectively. Compared with other related algorithms, DANet has significant advantages in SAR image target detection accuracy.

Key words: synthetic aperture radar (SAR), attention mechanism, object detection, feature pyramid, loss function

CLC Number:

TP 751

Kang NI, Wenjie JIA, Minrui ZOU, Zhizhong ZHENG. SAR object detection based on dynamic aggregation network[J]. Systems Engineering and Electronics, 2025, 47(8): 2527-2539.

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类别	训练集	验证集	合计
船只	27459	12399	39858
油罐	8153	4166	12319
桥梁	1284	567	1851
飞机	5006	1362	6368
合计	41902	18494	6368

类别	训练集	验证集	测试集	合计
A330	251	27	31	309
A320/321	1637	82	52	1771
A220	3023	247	460	3730
ARJ21	447	378	362	1187
Boeing737	1755	252	550	2557
Boeing787	1930	261	454	2645
Other	2508	715	1041	4246
合计	11551	1962	2950	16363

对比算法	mAP/%	DR/%	FAR/%	FLOPS（×10⁸）	FPS	参数量（×10⁶）
ATSS^[5]	68.00	71.43	35.22	12.89	180.72	32.12
Deformable_DETR^[36]	65.02	73.51	43.57	14.62	107.61	40.10
Faster R-CNN^[7]	55.69	57.83	47.09	26.13	138.24	41.36
FCOS^[8−9]	67.52	72.10	35.27	12.89	195.97	32.12
GFL^[37]	67.24	70.75	37.25	13.08	178.14	32.27
PAA^[38]	63.37	68.73	42.10	12.89	106.52	32.12
RepPoints^[10]	49.25	56.05	57.21	12.12	106.99	36.82
RetinaNet^[11]	33.71	52.28	76.23	13.14	186.30	36.39
YOLOF^[13]	46.39	50.75	59.55	6.58	287.21	42.41
YOLOv8	58.86	58.79	36.21	8.10	303.41	30.06
DANet（本文方法）	70.25	73.23	33.50	12.94	179.41	33.25

对比算法	mAP/%	DR/%	FAR/%	FLOPS（×10⁸）	FPS	参数量（×10⁶）
ATSS^[5]	46.89	66.06	75.10	206.32	37.24	32.13
Deformable_DETR^[36]	43.53	83.31	94.21	200.40	25.43	40.10
Faster R-CNN^[7]	48.49	64.87	70.09	211.05	36.75	41.38
FCOS^[8−9]	48.69	72.34	73.29	206.32	38.36	32.13
GFL^[37]	45.49	65.30	76.10	209.49	37.18	32.27
PAA^[38]	47.67	80.03	88.12	206.32	16.01	32.13
RepPoints^[10]	51.11	77.89	74.56	193.98	30.89	36.82
RetinaNet^[11]	46.09	73.41	81.12	211.53	38.62	36.45
YOLOF^[13]	48.86	75.27	79.41	105.30	59.51	42.48
YOLOv8	49.73	68.02	70.34	140.23	62.78	30.07
DANet（本文方法）	52.36	73.93	69.51	206.54	37.42	33.26

类别	目标数量	FCOS			DANet
类别	目标数量	AP	DR	FAR	AP	DR	FAR
船只	12399	89.17	91.30	11.25	89.31	91.40	11.13
油罐	5935	63.28	68.91	38.05	63.28	68.82	37.59
桥梁	567	73.92	80.82	40.01	79.01	82.67	37.55
飞机	1363	43.72	47.79	50.77	49.41	50.53	48.02

SAR object detection based on dynamic aggregation network

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$\sigma $	MSAR-1.0	SAR-AIRcraft-1.0
0.95	67.77	49.80
0.995	67.89	49.56
0.9995	68.40	51.47
0.99995	67.87	50.16

$\delta $	$\alpha $	MSAR-1.0	SAR-AIRcraft-1.0
1.4	5	67.65	51.01
1.6	4	68.40	51.47
1.9	3	68.82	51.40
2.5	2	68.72	50.61

动态聚合模块	类平衡动态IoU损失	mAP	DR	FAR
×	×	67.52	72.10	35.27
√	×	69.30	73.08	34.79
×	√	68.82	73.11	35.48
√	√	70.25	73.23	33.50

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类别	目标数量	FCOS			DANet
类别	目标数量	AP	DR	FAR	AP	DR	FAR
A330	31	73.52	83.91	28.43	82.63	90.33	26.38
A320/321	52	48.81	78.83	86.06	60.11	88.51	69.63
A220	450	50.48	83.92	81.82	54.21	82.22	73.95
ARJ21	362	50.02	61.58	61.60	47.28	56.91	64.69
Boeing737	550	31.90	61.62	94.34	31.84	60.03	93.02
Boeing787	454	36.04	60.79	83.05	37.70	65.24	84.72
其他	1041	50.10	75.79	77.73	52.76	74.43	74.18