CLC number: TN957.51
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-05-04
Cited: 0
Clicked: 6378
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images",
author="Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="4",
pages="630-643",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000611"
}
%0 Journal Article
%T Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images
%A Xiaolong CHEN
%A Xiaoqian MU
%A Jian GUAN
%A Ningbo LIU
%A Wei ZHOU
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 4
%P 630-643
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000611
TY - JOUR
T1 - Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images
A1 - Xiaolong CHEN
A1 - Xiaoqian MU
A1 - Jian GUAN
A1 - Ningbo LIU
A1 - Wei ZHOU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 630
EP - 643
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000611
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).
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