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