一种目标区域特征增强的SAR图像飞机目标检测与识别网络

韩萍; 赵涵; 廖大钰; 彭彦文; 程争

doi:10.11999/JEIT240491

一种目标区域特征增强的SAR图像飞机目标检测与识别网络

doi: 10.11999/JEIT240491

中国民航大学智能信号与图像处理天津市重点实验室天津 300300

基金项目: 中央高校基金(3122020043)

详细信息

作者简介:
韩萍：女，教授，研究方向为SAR图像处理与目标检测

赵涵：男，硕士生，研究方向为SAR图像飞目标检测

廖大钰：男，硕士生，研究方向为SAR图像飞机目标检测

彭彦文：男，硕士生，研究方向为PoLSAR图像飞机场跑道检测

程争：男，实验师，研究方向为极化SAR图像处理与目标检测

通讯作者:
韩萍　hanpingcauc@163.com

中图分类号: TN958
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- 被引次数: 0
出版历程
- 收稿日期: 2024-06-14
- 修回日期: 2024-11-21
- 网络出版日期: 2024-11-25

A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement

Tianjin Key Laboratory of Intelligent Signal and Image Processing, Civil Aviation University of China, Tianjin 300300, China

Funds: The Central University Fund (3122020043)

摘要

摘要: 在合成孔径雷达(SAR)图像飞机目标检测识别中，飞机目标图像呈现离散特性以及结构之间的相似性会降低飞机检测与识别的准确率。为此该文设计了一种目标区域特征增强的SAR图像飞机目标检测与识别网络。网络由3部分组成：保护飞机特征的跨阶段部分网络(FP-CSPDarnet)、自适应特征融合的特征金字塔(FPN-A)以及目标区域散射特征提取与增强的检测头(D-Head)。FP-CSPDarnet在提取特征的同时可以有效保护SAR图像飞机特征；FPN-A采用多层次特征自适应融合、细化，来增强飞机特征；D-Head在检测前有效增强飞机可辨别特征，提升飞机检测与识别精度。利用SAR-ADRD数据集的实验结果证明了该文所提方法有效性，其平均精度相对与基线网络YOLOv5s提升了2.0%。
- 合成孔径雷达 /
- 飞机目标检测与识别 /
- YOLOv5s /
- 飞机特征保护 /
- 特征增强
Abstract: In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
- Synthetic Aperture Radar(SAR) /
- Aircraft target detection and recognition /
- YOLOv5s /
- Aircraft feature protection /
- Target area feature enhancement

HTML全文

图 1 SAR图像中飞机的结构展示

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图 2 YOLOv5网络架构图

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图 3 FPN架构图

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图 4 本文网络架构图

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图 5 CSPDarknet骨干网络与FP-CSPDarnet结构对比图

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图 6 SPD-Conv结构图

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图 7 FPN-A结构图

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图 8 AR模块

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图 9 D-Head结构图

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图 10 AF模块

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图 11 数据集中飞机的类别与数量

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图 12 骨干网络输出特征图通道可视化结果图

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图 13 优化检测头前后的混淆矩阵

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图 14 各个网络检测结果图1

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图 15 各个网络检测结果图2

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表 1 YOLOv5网络深度消融实验

模型	P(%)	R(%)	mAP(%)	Parameters(M)	fps
YOLOv5n	90.6	89.1	92.0	1.8	93.6
YOLOv5s	93.0	90.6	93.7	7.0	93.5
YOLOv5m	92.2	90.2	93.3	20.9	68.5
YOLOv5l	93.1	91.2	93.0	46.1	47.6
YOLOv5x	94.0	91.0	94.4	86.2	41.9

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表 2 网络各个模块消融实验

	FPN-A	FP-CSPDarnet	D-Head	P(%)	R(%)	mAP(%)	Parameters(M)	fps
Baseline	–	–	–	93.0	90.6	93.7	7.0	93.6
	√	–	–	90.2	86.5	92.4	8.5	129.9
	√	√	–	92.3	92.5	94.8	10.3	129.9
本文方法	√	√	√	92.5	92.3	95.7	12.2	108.7

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表 3 骨干网络P-CSPDarknet中各模块消融实验(%)

FPN-A	FP-CSPDarnet (骨干结构)	FP-CSPDarnet (骨干结构+SPD-Conv)	P (%)	R (%)	mAP
√	–	–	90.2	86.5	92.4
√	√	–	91.8	90.2	93.9
√	√	√	92.3	92.5	94.8

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表 4 不同检测网络对比实验

	P(%)	R(%)	mAP(%)	Parameters(M)	fps
YOLOv5s	92.6	89.9	93.7	7.0	93.5
Faster R-CNN	82.0	85.6	87.8	41.2	11.2
TOOD	84.9	81.7	85.0	31.8	12.9
YOLOX-s	80.7	83.4	89.7	8.9	41.2
YOLOv7s	91.1	87.6	93.5	9.2	75.8
本文方法	92.5	92.3	95.7	12.3	108.7

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表 5 数据集内不同飞机类别在不同检测网络的精度(%)

网络模型/飞机类别	Boeing787	A220	ARJ21	Boeing737-800	A320/321	A330	Others	mAP(%)
YOLOv5s	98.0	96.6	93.6	87.2	88.1	95.1	96.9	93.7
Yolov7s	96.8	95.5	92.2	93.0	85.8	96.2	94.2	93.5
Tood	90.7	94.3	85.3	81.1	67.8	91.0	84.4	84.9
YOLOX-S	89.2	86.4	86.3	94.5	95.4	86.7	89.5	89.7
Faster-R-CNN	91.8	94.8	87.9	85.6	74.7	91.1	89.0	87.8
本文方法	98.7	98.7	97.7	95.1	95.4	94.8	97.1	95.7

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