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Small-scale aircraft detection in remote sensing images based on Faster-RCNN

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Abstract

Detecting aircraft in remote sensing images becomes increasingly important in both military and civilian fields. However, the accuracy of existing detection approach is not high enough especially for the small-scale aircraft when considering the size and scenario of the remote sensing images. To improve the accuracy of detecting small-scale aircraft, this paper proposes a detection approach for aircraft based on Faster-RCNN, called MFRC. Firstly, the K-means algorithm is used to cluster aircraft data in remote sensing images. Anchors are improved based on clustering results. Secondly, to extract location features of small-scale aircraft, the layer of pooling in the VGG16 network is reduced from four to two. Finally, the Soft-NMS algorithm is used to optimize the aircraft bounding boxes. In the experimentation, MFRC is evaluated under different conditions and compared with other models. The experimental results show that MFRC can detect small-scale aircraft effectively and the accuracy is improved by 3% compared to existing methods.

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Acknowledgements

The authors also gratefully acknowledge the insightful comments and suggestions of the reviewers, which have improved the presentation. The authors would like to thank Mr. Feiyao Xu for participated in writing or technical editing of the manuscript.

Funding

The authors did not receive support from any organization for the submitted work. The authors have no relevant financial or non-financial interests to disclose.

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  1. School of Information Science and Engineering, Hebei University of Science and Technology, Hebei, 050018, China

    Yang Zhang, Chenglong Song & Dongwen Zhang

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  1. Yang Zhang
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  2. Chenglong Song
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Correspondence to Yang Zhang.

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Zhang, Y., Song, C. & Zhang, D. Small-scale aircraft detection in remote sensing images based on Faster-RCNN. Multimed Tools Appl 81, 18091–18103 (2022). https://doi.org/10.1007/s11042-022-12609-5

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