SAR Target Detection Based on Improved SSD with Saliency Map and Residual Network

Abstract

A target detection method based on an improved single shot multibox detector (SSD) is proposed to solve insufficient training samples for synthetic aperture radar (SAR) target detection. We propose two strategies to improve the SSD: model structure optimization and small sample augmentation. For model structure optimization, the first approach is to extract deep features of the target with residual networks instead of with VGGNet. Then, the aspect ratios of the default boxes are redesigned to match the different targets’ sizes. For small sample augmentation, besides the routine image processing methods, such as rotating, translating, and mirroring, enough training samples are obtained based on the saliency map theory in machine vision. Lastly, a simulated SAR image dataset called Geometric Objects (GO) is constructed, which contains dihedral angles, surface plates and cylinders. The experimental results on the GO-simulated image dataset and the MSTAR real image dataset demonstrate that the proposed method has better performance in SAR target detection than other detection methods.

Intelligent Detection System for Electrical Equipment based on Deep Learning and Infrared Image Processing Technology

“…In the study by Zhou et al, SSD can use residual networks for image feature extraction when detecting sample targets. This method exhibits stronger target detection ability and higher detection accuracy in performance comparison [17].…”