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ElDet: An Anchor-Free General Ellipse Object Detector

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

Ellipse detection is a fundamental task in object shape analysis. Under complex environments, the traditional image processing based approaches may under-perform due to the hand-crated features. Instead, CNN-based approaches are more robust and powerful. In this paper, we introduce an efficient anchor-free data-augmentation based general ellipse detector, termed ElDet. Different from existing CNN-based methods, our ElDet relies more on edge information which could excavate more shape information into learning. Specifically, we first develop an edge fusion module to composite an overall edge map which has more complete boundary and better continuity. The edge map is treated as augmentation input for our ElDet for ellipse regression. Secondly, three loss functions are tailored to our ElDet, which are angle loss, IoU loss, and binary mask prediction loss to jointly improve the ellipse detection performance. Moreover, we contribute a diverse ellipse dataset by collecting multiple classes of elliptical objects in real scenes. Extensive experiments show that the proposed ellipse detector is very competitive to state-of-the-art methods.

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Author information

Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, China

    Tianhao Wang, Xiaohui Yuan & Siyu Xia

  2. College of Engineering and Computer Science, The Australian National University, Canberra, Australia

    Changsheng Lu

  3. Computer and Information Science Department, University of Massachusetts Dartmouth, Dartmouth, USA

    Ming Shao

Authors
  1. Tianhao Wang
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  2. Changsheng Lu
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  3. Ming Shao
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  4. Xiaohui Yuan
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  5. Siyu Xia
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Corresponding author

Correspondence to Siyu Xia .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Wang, T., Lu, C., Shao, M., Yuan, X., Xia, S. (2023). ElDet: An Anchor-Free General Ellipse Object Detector. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_14

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  • Print ISBN: 978-3-031-26312-5

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