In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed.

中文翻译：

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一种基于高效双向路径聚合注意力网络的SAR图像快速飞机检测方法

在合成孔径雷达 (SAR) 图像的飞机检测中，存在几个主要挑战：飞机的破碎特征、尺寸异质性和复杂背景的干扰。为了解决这些问题，提出了一种高效的双向路径聚合注意网络（EBPA2N）。在EBPA2N中，以YOLOv5s为基础网络，提出并系统集成了Involution Enhanced Path Aggregation（IEPA）模块和Effective Residual Shuffle Attention（ERSA）模块，以提高飞行器的检测精度。IEPA 模块旨在有效提取高级语义和空间信息，以更好地捕捉飞机的多尺度散射特征。然后，轻量级的ERSA模块进一步增强提取的特征，克服复杂背景和斑点噪声的干扰，从而减少误报。为了验证所提出网络的有效性，实验中使用了分辨率为 1 m 的高分 3 机场 SAR 数据。我们的EBPA2N算法的检测率和误报率分别为93.05%和4.49%，优于最新的EfficientDet-D0和YOLOv5s网络，并且在检测速度上也有优势。