Abstract
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.
Highlights
Synthetic aperture radar (SAR) can provide continuous and stable observation all day and all night, which has been widely used in various fields [1]
Our experiment has proved the outstanding performance of EBPA2N, which indicates the success of implementing multi-scale SAR image analytics as geospatial attention within deep neural networks
This paper proposes an effective aircraft detection network for large-scale SAR images, which greatly improves the accuracy of aircraft detection and provides fast detection of aircraft
Summary
Synthetic aperture radar (SAR) can provide continuous and stable observation all day and all night, which has been widely used in various fields [1]. Timely aircraft detection plays a pivotal role in airport management and military activities [2]. This is because the scheduling and placement of aircraft are sensitive spatio-temporally. There are three major challenges in aircraft detection: the shattered image features of aircraft, their size heterogeneity, and the interference of complex background. Small aircraft are more likely to be missed, resulting in a lower detection rate of the algorithm. Facilities around the aircraft could be recorded as features similar to those of the aircraft due to scattering [3], which further increases the difficulty of aircraft detection in SAR images. It is essential for detection algorithms to recognize effective features of the aircraft
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