| Citation: | LI Guangshuai, SU Juan, LI Yihonget al. An aircraft detection algorithm in SAR image based on improved Faster R-CNN[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(1): 159-168. doi: 10.13700/j.bh.1001-5965.2020.0004(in Chinese)
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In the field of Synthetic Aperture Radar (SAR) image analysis, as an important target, aircraft detection has attracted more and more attention. In order to solve the problem that traditional aircraft detection algorithms need to design hand-crafted features and have poor robustness, this paper proposes an aircraft detection algorithm based on improved Faster R-CNN. In this paper, a SAR Aircraft Dataset (SAD) is made. With Faster R-CNN as the detection framework, the improved k-means algorithm is used to design a more reasonable prior anchor frame to adapt to the characteristic of aircraft size. Based on the idea of inception module, multiple convolution kernels of different sizes are designed to expand the network width and enhance the expression of shallow features. By analyzing the residual network, the feature-map of Layer5 has a larger receptive field, and feature fusion is carried out after upsampling to make use of more context information. Meanwhile, the RoI Align unit proposed in Mask R-CNN algorithm is introduced to eliminate the mapping deviation between the feature-map and the original image. The experimental results show that, compared with the original Faster R-CNN algorithm, the proposed algorithm improves the average detection accuracy by 7.4% on the SAD, while maintaining a fast detection speed.
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