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MEAD: a Mask-guidEd Anchor-free Detector for oriented aerial object detection

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Abstract

Object detection in aerial images is a challenging task due to various orientations of objects and the lack of discriminative features. Existing methods are usually in a dilemma between accuracy and speed. While one-stage anchor-free detectors inference more quickly than two-stage frameworks, their predictions are not as accurate as that of the opposite. This paper proposes a quick and accurate detector, Mask-guidEd Anchor-free Detector (MEAD). It can rapidly locate oriented objects in aerial images by means of per-pixel prediction. Furthermore, it embeds a cascade architecture to locate targets more precisely. To enhance feature discrimination, the mask-guided branch is employed to force features to attend the foreground regions. Comparative experiments are conducted on DOTA and HRSC2016 datasets. The results show that MEAD is better than current state-of-the-art anchor-free detectors, that is, mAP 74.33 on DOTA and 89.83 on HRSC2016.

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  1. https://github.com/CAPTAIN-WHU/DOTA_devkit

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Acknowledgements

The authors are thankful for the financial support from the National Key Research and Development Program of China (2018YFB1404400), and the National Natural Science Foundation of China (Grant No. 61906190, U1936206, 61976213 and 61976212).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zewen He, Zhida Ren, Xuebing Yang, Yang Yang & Wensheng Zhang

  2. School of Artificial Intelligence, University of Chinese Academy of Science, Beijing, China

    Zewen He, Zhida Ren & Wensheng Zhang

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  1. Zewen He
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  2. Zhida Ren
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  3. Xuebing Yang
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  4. Yang Yang
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  5. Wensheng Zhang
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Correspondence to Wensheng Zhang.

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He, Z., Ren, Z., Yang, X. et al. MEAD: a Mask-guidEd Anchor-free Detector for oriented aerial object detection. Appl Intell 52, 4382–4397 (2022). https://doi.org/10.1007/s10489-021-02570-5

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