Attention feature fusion awareness network for vehicle target detection in SAR images

ABSTRACT

Synthetic aperture radar (SAR) target detection plays a crucial role in military surveillance, earth observation, and disaster monitoring. With the development of deep learning (DL) and SAR imaging technology, numerous SAR target detection methods have been proposed and achieved better detection results. However, detecting different categories of SAR vehicle targets is still challenging due to the influence of coherent speckle noises and background clutter. This article presents a novel attention feature fusion awareness network (AFFNet) for vehicle target detection in SAR images. Specifically, we propose a multi-scale semantic attention (MSSA) module to obtain multi-scale and semantic features of target region; the variable multi-scale feature fusion (VMSFF) module is introduced to effectively fuse different feature information and alleviate target deformation interference by establishing feature correlation; the part feature awareness (PFA) module is used to obtain unique attribute of different vehicle targets to generate accurate anchor boxes. In addition, we design a candidate boundary box selection scheme, which can effectively adapt to SAR targets with different scales and categories. Overall, AFFNet is designed based on the SAR imaging mechanism and target physical feature information. To evaluate the performance of the proposed method, extensive experiments are conducted on the MSTAR dataset. The experiment results show that the proposed AFFNet obtains the mAP of 98.36$\mathrm{\%}$ and 97.26$\mathrm{\%}$ on standard operating conditions (SOCs) and extended operating conditions (EOCs), which is more efficient than the other state-of-the-art methods.

KEYWORDS:

• Synthetic aperture radar (SAR)
• deep learning
• vehicle target detection
• feature awareness
• feature fusion

Acknowledgements

All authors would sincerely thank the reviewers and editors for their beneficial, careful, and detailed comments and suggestions for improving the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42201077,61671465,62172338].