Full Text:   <2248>

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CLC number: TN957.51

On-line Access: 2022-04-21

Received: 2020-11-06

Revision Accepted: 2020-12-23

Crosschecked: 2022-05-04

Cited: 0

Clicked: 3577

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiaolong CHEN

https://orcid.org/0000-0002-1040-1655

Xiaoqian MU

https://orcid.org/0000-0002-5956-3356

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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.630-643

http://doi.org/10.1631/FITEE.2000611


Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images


Author(s):  Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU

Affiliation(s):  Marine Target Detection Research Group, Naval Aviation University, Yantai 264001, China

Corresponding email(s):   cxlcxl1209@163.com, guanjian_68@163.com

Key Words:  Marine target detection, Navigation radar, Plane position indicator (PPI) images, Convolutional neural network (CNN), Faster R-CNN (region convolutional neural network) method


Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU. Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 630-643.

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year="2022",
publisher="Zhejiang University Press & Springer",
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A1 - Wei ZHOU
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Abstract: 
As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar plane position indicator (PPI) images, it is difficult to achieve good performance. In this paper, taking navigation radar PPI images as an example, a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.‍g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network (CNN). First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) navigation radar was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for navigation radar. Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).

基于海面更快区域卷积神经网络的导航雷达平面位置指示器图像海面目标检测方法

陈小龙,牟效乾,关键,刘宁波,周伟
海军航空大学海上目标探测课题组,中国烟台市,264001
摘要:更快的区域卷积神经网络(Faster R-CNN)作为一种经典深度学习目标检测算法,已广泛应用于高分辨率合成孔径雷达和逆合成孔径雷达的图像检测。然而,对于大多数常见的低分辨率雷达平面位置指示器(PPI)图像,很难取得良好性能。本文以导航雷达PPI图像为例,针对复杂背景(如海杂波)和目标特性情况,提出一种基于海面的更快的区域卷积神经网络(Marine-Faster R-CNN)算法的海面目标检测方法。该方法利用卷积神经网络(CNN)对雷达回波生成的PPI图像进行特征提取和目标识别。首先,为提高检测海面目标的准确率,降低虚警率,设计了特征融合网络FFNet(Feature Fusion Network),并从锚点尺度、多目标检测、数据样本平衡性、尺度归一化等方面优化Faster R-CNN,并基于此建立Marine-Faster R-CNN海面目标检测模型。然后,利用日本无线电株式会社(JRC)导航雷达采集不同条件下的回波数据,构建海面目标数据集。最后,与经典Faster R-CNN方法和恒虚警率算法对比,证明所提方法准确率更高,稳健性更佳,泛化能力更强,可应用于导航雷达海面目标检测。它的性能经过了来自不同观测条件(海况、雷达参数和不同目标)数据集的验证。

关键词:海面目标检测;导航雷达;平面位置指示器(PPI)图像;卷积神经网络;更快的区域卷积神经网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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