基于深度分离卷积神经网络的高速高精度SAR舰船检测

张晓玲; 张天文; 师君; 韦顺军

doi:10.12000/JR19111

基于深度分离卷积神经网络的高速高精度SAR舰船检测

DOI: 10.12000/JR19111

电子科技大学信息与通信工程学院成都 611731

基金项目: 国家自然科学基金(61571099, 61501098, 61671113)，国家重点研发计划(2017YFB0502700)

详细信息

作者简介:
张晓玲(1964–)，女，四川人，获电子科技大学工学博士学位，目前为电子科技大学教授/博导，主要从事SAR成像技术、雷达探测技术研究、3维SAR成像的目标散射特性(RCS)反演。E-mail: xlzhang@uestc.edu.cn

张天文(1994–)，男，江苏人，现于电子科技大学信息与通信工程学院攻读博士学位，主要研究领域为SAR成像技术、遥感图像处理与智能识别解译。E-mail: twzhang@std.usetc.edn.cn

师　君(1979–)，男，河南人，获电子科技大学工学博士学位，目前为电子科技大学副教授，主要从事SAR数据处理方面研究。E-mail: shijun@uestc.edu.cn

韦顺军(1983–)，男，广西人，获电子科技大学工学博士学位，目前为电子科技大学副教授，主要从事SAR成像技术、干涉SAR技术研究。E-mail: weishunjun@uestc.edu.cn

通讯作者:
张晓玲 xlzhang@uestc.edu.cn

中图分类号: TN957.52
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出版历程
- 收稿日期: 2019-12-16
- 修回日期: 2019-12-23
- 网络出版日期: 2019-12-01

High-speed and High-accurate SAR Ship Detection Based on a Depthwise Separable Convolution Neural Network

School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (61571099, 61501098, 61671113), The National Key R&D Program of China (2017YFB0502700)

More Information

Corresponding author: ZHANG Xiaoling, xlzhang@uestc.edu.cn

摘要

摘要: 随着人工智能的兴起，利用深度学习技术实现SAR舰船检测，能够有效避免传统的复杂特征设计，并且检测精度获得了极大的改善。然而，现如今大多数检测模型往往以牺牲检测速度为代价来提高检测精度，限制了一些SAR实时性应用，如紧急军事部署、迅速海难救援、实时海洋环境监测等。为了解决这个问题，该文提出一种基于深度分离卷积神经网络(DS-CNN)的高速高精度SAR舰船检测方法SARShipNet-20，该方法取代传统卷积神经网络(T-CNN)，并结合通道注意力机制(CA)和空间注意力机制(SA)，能够同时实现高速和高精度的SAR舰船检测。该方法在实时性SAR应用领域具有一定的现实意义，并且其轻量级的模型有助于未来的FPGA或DSP的硬件移植。
- 卷积神经网络 /
- 深度分离卷积神经网络 /
- SAR /
- 舰船检测 /
- 注意力机制
Abstract: With the development of artificial intelligence, Synthetic-Aperture Radar (SAR) ship detection using deep learning technology can effectively avoid traditionally complex feature design and thereby greatly improve detection accuracy. However, most existing detection models often improve detection accuracy at the expense of detection speed that limits some real-time applications of SAR such as emergency military deployment, rapid maritime rescue, and real-time marine environmental monitoring. To solve this problem, a high-speed and high-accuracy SAR ship detection method called SARShipNet-20 based on a Depthwise Separable Convolution Neural Network (DS-CNN) has been proposed in this paper, that replaces the Traditional Convolution Neural Network (T-CNN) and combines Channel Attention (CA) and Spatial Attention (SA). As a result, high-speed and high-accuracy SAR ship detection can be simultaneously achieved. This method has certain practical significance in the field of real-time SAR application, and its lightweight model is helpful for future FPGA or DSP hardware transplantation.
- Convolution Neural Network (CNN) /
- Depthwise Separable Convolution Neural Network (DS-CNN) /
- SAR /
- Ship detection /
- Attention mechanism

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图 1 传统卷积神经网络和深度分离卷积神经网络示意图

Figure 1. Diagrammatic sketch of T-CNN and DS-CNN

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图 2 网络结构示意图 (SARShipNet-20)

Figure 2. Network structure (SARShipNet-20)

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图 3 卷积层内部操作流程

Figure 3. Internal operation flow in convolution layers

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图 4 通道注意力机制

Figure 4. Channel Attention (CA)

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图 5 空间注意力机制

Figure 5. Spatial Attention (SA)

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图 6 SARShipNet-20的SAR舰船检测结果

Figure 6. SAR ship detection results of SARShipNet-20

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图 7 SARShipNet-20性能评价曲线

Figure 7. Performance evaluation curve of SARShipNet-20

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表 1 SARShipNet-20的SAR舰船检测结果评价指标

Table 1. Evaluation index of SAR ship detection results of SARShipNet-20

类型	GT	TP	FN	FP	P_d (%)	P_m (%)	P_f (%)	Recall (%)	Precision (%)	mAP (%)	Time (ms)
T-CNN	184	180	4	8	97.83	2.17	4.26	97.83	95.74	96.88	10.14
DS-CNN	184	175	9	23	95.11	4.89	11.62	95.11	88.38	93.64	4.54
DS-CNN + CA	184	179	5	29	97.28	2.72	13.94	97.28	89.06	95.78	5.68
DS-CNN + SA	184	178	6	11	96.74	3.26	5.82	96.74	94.18	95.64	6.67
DS-CNN + CA + SA	184	180	4	8	97.83	2.17	4.26	97.83	95.74	96.93	8.72

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表 2 不同方法的检测性能对比

Table 2. Comparison of detection performance of different methods

方法	P_d (%)	P_m (%)	P_f (%)	Recall (%)	Precision (%)	mAP (%)	Time (ms)
Faster R-CNN^[16]	85.16	14.84	18.85	85.16	81.15	82.66	327.48
RetinaNet^[34]	96.70	3.30	6.88	96.70	93.12	95.68	314.43
R-FCN^[35]	95.65	4.35	7.37	95.65	92.63	95.15	178.16
SSD^[18]	94.51	5.49	14.85	94.51	85.15	92.67	48.86
YOLOv3^[20]	96.70	3.30	6.38	96.70	93.62	95.34	22.30
YOLOv1^[28]	84.07	15.93	15.47	84.07	84.53	81.24	21.95
YOLOv2^[29]	92.86	7.14	15.08	92.86	84.92	90.09	19.01
YOLOv3-tiny^[20]	70.33	29.12	22.29	70.33	77.58	64.64	10.25
YOLOv2-tiny^[29]	47.80	52.20	26.27	47.80	73.73	44.40	9.43
SARShipNet-20(本文方法)	97.83	2.17	4.26	97.83	95.74	96.93	8.72

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表 3 不同方法的模型对比

Table 3. Model comparison of different methods

方法	网络参数的数量	浮点运算量(FLOPs)	模型大小 (MB)
Faster R-CNN	272,746,867	545,429,460	752.75
RetinaNet	61,576,342	307,592,895	235.44
R-FCN	50,578,686	101,385,166	193.04
SSD	47,663,806	95,040,404	181.24
YOLOv3	36,382,957	72,545,184	139.25
YOLOv1	28,342,195	46,981,897,900	108.54
YOLOv2	23,745,908	118,685,133	90.73
YOLOv3-tiny	15,770,510	31,608,360	60.22
YOLOv2-tiny	8,676,244	86,692,284	33.20
SARShipNet-20(本文方法)	5,867,737	11,699,792	23.17

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