Abstract
A target detection method based on improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. Firstly, a residual network is introduced in the feature extraction structure for extracting deep features of the target; then, the aspect ratio of default boxes is redesigned to match different sizes of target detection; finally, the existing dataset is expanded with small samples based on saliency map. The experimental results based on the simulated SAR data and the real SAR data prove that the proposed method has better performance in SAR target detection.
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