基于YOLO的航管一次雷达目标检测方法

doi:10.12305/j.issn.1001-506X.2024.01.17

摘要/Abstract

摘要：

针对传统恒虚警率(constant false alarm rate, CFAR)检测方法检测率低的问题, 提出一种基于YOLO(you only look once)的深度学习雷达目标检测方法。首先, 利用同相正交(in-phase/quadrature, I/Q)数据匹配滤波后形成的雷达原始图像自建雷达目标图像数据集。然后, 改进YOLO检测模型的网络结构、特征融合策略和损失函数以提高模型的精度, 并引入迁移学习思想, 利用预训练的深度学习网络提取图像特征, 降低了检测模型对训练样本量的要求。最后, 在自建数据集上对YOLO目标检测方法进行了实验验证。航管一次雷达实测数据的实验证明: 与传统CFAR检测方法和两阶段的快速区域卷积神经网络(region convolutional neural networks, R-CNN)检测方法相比, 所提方法的目标检测率大幅提高, 虚警率明显降低, 且实现了实时检测。

关键词: 航管一次雷达, 深度学习, 目标检测, YOLO

Abstract:

Aiming at the low detection rate of traditional constant false alarm rate (CFAR) detection methods, a deep learning radar target detection method based on you only look once (YOLO) is proposed. Firstly, the radar target image dataset is constructed by using the original radar image formed by in-phase/quadrature (I/Q) data matching filtering. Then, the network structure, feature fusion strategy, and loss function of the YOLO detection model are improved to improve the accuracy of the model. And the idea of transfer learning is introduced to extract image features using the pre-trained deep learning network, which reduced the requirement of the detection model on the training sample size. Finally, the YOLO target detection method is experimentally verified on the self-built dataset. The experimental results on the measured data of the primary surveillance radar show that, compared with the traditional CFAR detection method and the two-stage faster region convolutional neural networks (R-CNN) detection method, the target detection rate of the proposed method is greatly improved, the false alarm rate is significantly reduced, and the real-time detection is realized.

Key words: primary surveillance radar, deep learning, target detection, you only look once (YOLO)

中图分类号:

TN957

施端阳, 林强, 胡冰, 杜小帅. 基于YOLO的航管一次雷达目标检测方法[J]. 系统工程与电子技术, 2023, 46(1): 143-151.

Duanyang SHI, Qiang LIN, Bing HU, Xiaoshuai DU. Target detection method of primary surveillance radar based on YOLO[J]. Systems Engineering and Electronics, 2023, 46(1): 143-151.

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参考文献 31

1	何友, 关键, 孟祥伟, 等. 雷达自动检测和CFAR处理方法综述[J]. 系统工程与电子技术, 2001, 23 (1): 9- 14. doi: 10.3321/j.issn:1001-506X.2001.01.003
	HE Y , GUAN J , MENG X W , et al. Survey of automatic radar detection and CFAR processing[J]. Systems Engineering and Electronics, 2001, 23 (1): 9- 14. doi: 10.3321/j.issn:1001-506X.2001.01.003
2	ZHANG B Q , XIE J H , ZHOU W . A Bayesian CFAR detector for interference control in Weibull clutter[J]. Digital Signal Processing, 2020, 104, 102781. doi: 10.1016/j.dsp.2020.102781
3	GOURI A , MEZACHE A , OUDIRA H . Radar CFAR detection in Weibull clutter based on zlog(z) estimator[J]. Remote Sensing Letters, 2020, 11 (6): 581- 589. doi: 10.1080/2150704X.2020.1744043
4	ZHANG B Q , ZHOU J , XIE J H , et al. Weighted likelihood CFAR detection for Weibull background[J]. Digital Signal Processing, 2021, 115, 103079. doi: 10.1016/j.dsp.2021.103079
5	ZEBIRI K , MEZACHE A . Radar CFAR detection for multiple-targets situations for Weibull and log-normal distributed clutter[J]. Signal, Image and Video Processing, 2021, 15 (8): 1671- 1678. doi: 10.1007/s11760-021-01905-6
6	许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9 (4): 684- 714.
	XU S W , BAI X H , GUO Z X , et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9 (4): 684- 714.
7	时艳玲, 水鹏朗. 海面漂浮小目标的特征联合检测算法[J]. 电子与信息学报, 2012, 34 (4): 871- 877.
	SHI Y L , SHUI P L . Feature united detection algorithm on floating small target of sea surface[J]. Journal of Electronics & Information Technology, 2012, 34 (4): 871- 877.
8	LI Y Z , XIE P C , TANG Z S , et al. SVM-based sea-surface small target detection: a false-alarm-rate-controllable approach[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (8): 1225- 1229. doi: 10.1109/LGRS.2019.2894385
9	郭子薰, 水鹏朗, 白晓惠, 等. 海杂波中基于可控虚警K近邻的海面小目标检测[J]. 雷达学报, 2020, 9 (4): 654- 663.
	GUO Z X , SHUI P L , BAI X H , et al. Sea-surface small target detection based on K-NN with controlled false alarm rate in sea clutter[J]. Journal of Radars, 2020, 9 (4): 654- 663.
10	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
11	GIRSHICK R. Fast R-CNN[C]//Proc. of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
12	REN S Q , HE K M , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137- 1149. doi: 10.1109/TPAMI.2016.2577031
13	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
14	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proc. of the European Conference on Computer Vision, 2016: 21-37.
15	LIN T Y , GOYAL P , GIRSHICK R , et al. Focal loss for dense object detection[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2020, 42 (2): 318- 327. doi: 10.1109/TPAMI.2018.2858826
16	施赛楠, 董泽远, 杨静, 等. 基于时频图自主学习的海面小目标检测[J]. 系统工程与电子技术, 2021, 43 (1): 33- 41.
	SHI S N , DONG Z Y , YANG J , et al. Sea-surface small target detection based on autonomic learning of time-frequency graph[J]. Systems Engineering and Electronics, 2021, 43 (1): 33- 41.
17	DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248-255.
18	HUANG X Q . Implementation of sensitivity time control signal processing for secondary radar[J]. Telecommunication Engineering, 2015, 55 (5): 570- 573. doi: 10.3969/j.issn.1001-893x.2015.05.018
19	SONG C , WANG B N , XIANG M S , et al. A general framework for slow and weak range-spread ground moving target indication using airborne multichannel high-resolution radar[J]. IEEE Trans.on Geoscience and Remote Sensing, 2022, 60, 5113616.
20	BHARAT K M , RAJESH K P . Bayesian fusion strategy for moving target detection in multichannel SAR framework[J]. Evolutionary Intelligence, 2022, 15 (2): 1411- 1424. doi: 10.1007/s12065-020-00445-1
21	RYU J , MA J . Automatic target recognition of military SAR images using super-resolution based convolutional neural network[J]. Journal of Institute of Control, Robotics and Systems, 2022, 28 (1): 22- 27. doi: 10.5302/J.ICROS.2022.21.0178
22	QU Q Z , WANG Y L , LIU W J , et al. A false alarm controllable detection method based on CNN for sea-surface small targets[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 4025705.
23	GRECO M , STINCO P , GINI F , et al. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. IEEE Trans.on Aerospace and Electronic Systems, 2010, 46 (3): 1502- 1513. doi: 10.1109/TAES.2010.5545205
24	CUI J Y , JIA H C , WANG H P , et al. A fast threshold neural network for ship detection in large-scene SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 6016- 6032. doi: 10.1109/JSTARS.2022.3192455
25	WANG D Y , ZHANG C K , HAN M . FIAD net: a fast SAR ship detection network based on feature integration attention and self-supervised learning[J]. International Journal of Remote Sensing, 2022, 43 (4): 1485- 1513. doi: 10.1080/01431161.2022.2042617
26	FU J M , SUN X , WANG Z R , et al. An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images[J]. IEEE Trans.on Geoscience and Remote Sensing, 2021, 59 (2): 1331- 1344. doi: 10.1109/TGRS.2020.3005151
27	CHEN S Q , ZHAN R H , WANG W , et al. Learning slimming SAR ship object detector through network pruning and know-ledge distillation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 1267- 1282. doi: 10.1109/JSTARS.2020.3041783
28	WEI S J , ZHOU Z C , WANG M , et al. 3DRIED: a high-resolution 3-D millimeter-wave radar dataset dedicated to imaging and evaluation[J]. Remote Sensing, 2021, 13 (17): 3366- 3386. doi: 10.3390/rs13173366
29	金添, 宋永坤, 戴永鹏, 等. UWB-HA4D-1.0: 超宽带雷达人体动作四维成像数据集[J]. 雷达学报, 2022, 11 (1): 27- 39.
	JIN T , SONG Y K , DAI Y P , et al. UWB-HA4D-1.0: an ultra-wideband radar human activity 4D imaging dataset[J]. Journal of Radars, 2022, 11 (1): 27- 39.
30	BHATT S , SONI H , PAWAR T , et al. Diagnosis of pulmonary nodules on CT images using YOLOv4[J]. International Journal of Online and Biomedical Engineering, 2022, 18 (5): 131- 146.
31	YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[EB/OL]. [2022-08-01]. arxiv. org/abs/1511.07122v3.

模型类别	检测率/%	虚警率/%	检测时间/s
CFAR	80.00	26.98	0.01
Faster R-CNN	87.14	6.19	0.67
改进的YOLO	96.67	5.24	0.15

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