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Feature-transferable Pyramid Network for Cross-scale Object Detection in SAR Images
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摘要: SAR图像多尺度目标检测能够实现大场景SAR图像中关键目标的定位与识别，是SAR图像解译的关键技术之一。然而针对尺寸相差较大的SAR目标的同时检测，即跨尺度目标检测问题，现有目标检测方法难以实现。该文提出一种基于特征转移金字塔网络(FTPN)的SAR图像跨尺度目标检测方法。在特征提取阶段采用特征转移方法，实现各层特征图的有效连接，实现不同尺度特征图的提取；同时采用空洞卷积群方法，增大特征提取的感受野，促使网络提取到大尺度目标特征。上述环节能够有效保留不同尺寸目标特征，从而实现SAR图像中跨尺度目标的同时检测。基于高分三号SAR数据、SSDD数据集及高分辨率SAR舰船检测数据集-2.0等数据集的试验表明，该文方法能够实现SAR图像中机场、舰船等跨尺度目标的检测，在已有数据集上mAP达96.5%，较特征金字塔网络算法提升8.1%，并且整体性能优于现阶段最新的YOLOv4等目标检测算法。Abstract: Multiscale object detection in Synthetic Aperture Radar (SAR) images can locate and recognize key objects in large-scene SAR images, and it is one of the key technologies in SAR image interpretation. However, for the simultaneous detection of SAR objects with large size differences, that is, cross-scale object detection, existing object detection methods are difficult to extract the features of cross-scale objects, and also difficult to realize cross-scale object simultaneous detection. In this study, we propose a multiscale object detection method based on the Feature-Transferable Pyramid Network (FTPN) for SAR images. In the feature extraction stage, the feature migration method is used to obtain an effective mosaic of the feature images of each layer and extract feature images with different scales. Simultaneously, the void convolution method is used to increase the receptive field of feature extraction and aid the network in extracting large object features. These steps can effectively preserve the features of objects of different sizes, to realize the simultaneous detection of cross-scale objects in SAR images. The experiments based on the GaoFen-3 SAR dataset, SAR Ship Detection Dataset (SSDD), and high-resolution SSDD-2.0 show that the proposed method can detect cross-scale objects, such as airports and ships in SAR images, and the mean Average Precision (mAP) can reach 96.5% on the existing dataset, which is 8.1% higher than that of the characteristic pyramid network algorithm. Moreover, the overall performance of the proposed method is better than that of the latest YOLOv4 and other object detection algorithms.
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Key words:
- SAR object detection /
- Feature pyramid /
- Feature-transfer /
- Dilated convolution group /
- Cross-scale
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图 6 空洞卷积群对检测结果的影响
Figure 6. Influence of dilated convolution group on detection results
图 9 大场景SAR图像跨尺度目标检测结果
Figure 9. Cross-scale object detection results in large scene SAR images
表 1 特征转移层对检测结果的影响
Table 1. The influence of feature-transfer layer on detection results
方法 mAP(%) 没有特征转移层 88.4 本文方法 92.8 
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表 2 空洞卷积群对检测结果的影响
Table 2. Influence of dilated convolution group on detection results
方法 mAP(%) 没有空洞卷积群 88.4 本文方法> 92.1
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表 3 与先进的目标检测网络相比
Table 3. Compared with advanced object detection networks
方法 mAP(%) Faster R-CNN 70.1 SSD 78.5 YOLOv4 88.2 YOLOv5 88.5 Improved Faster R-CNN 88.8 DAPN 89.8 PANet 90.8 SGE-centernet 93.9 本文方法 96.5
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表 4 机场目标和舰船目标的结果统计
Table 4. Result statistics for airport and ship objects
目标类型 N n m f DP(%) MP(%) FP(%) 机场 3 3 0 0 100 0 0 舰船 80 76 4 5 95 5 6.25
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表 5 单一尺度目标的检测性能
Table 5. Single scale object detection performance
单一尺度 mAP(%) 小尺度舰船 97.2 大尺度舰船 96.3 大尺度机场 94.4
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