In recent years,deep learning(DL)methods have been successfully applied to synthetic aperture radar(SAR)object detection task.Long-range,large-field imaging of SAR make it difficult to extract small target features,and the military field needs to obtain high-precision SAR target size information.In order to solve the above problems,an improved SAR image object detection algorithm based on YOLOv5 is proposed.Firstly,to solve the problem of false detection of small-scale objects and off-shore objects in complex scenes,an attention mechanism is introduced to explore the impact of different attention modules on the SAR objects detection.Then,according to the object arrangement characteristics of SAR images,an oriented object detection algorithm is constructed to predict the rotation angle of the target.Comparative experiments are carried out on the SAR ship detection dataset(SSDD)dataset,and the results show that the embedding of the attention module can effectively improve the detection accuracy by 2.5%.Compared with the conventional horizontal bounding box,the oriented bounding box achieves more promising performance for highly skewed objects and densely arranged objects.
deep learningsynthetic aperture radarobject detectionattention mechanismoriented bounding box
NETL
NSTL
万方数据
