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ObjectBox: From Centers to Boxes for Anchor-Free Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which are more biased toward specific object scales in their label assignments, we use only object center locations as positive samples and treat all objects equally in different feature levels regardless of the objects’ sizes or shapes. Specifically, our label assignment strategy considers the object center locations as shape- and size-agnostic anchors in an anchor-free fashion, and allows learning to occur at all scales for every object. To support this, we define new regression targets as the distances from two corners of the center cell location to the four sides of the bounding box. Moreover, to handle scale-variant objects, we propose a tailored IoU loss to deal with boxes with different sizes. As a result, our proposed object detector does not need any dataset-dependent hyperparameters to be tuned across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012 datasets, and compare our results to state-of-the-art methods. We observe that ObjectBox performs favorably in comparison to prior works. Furthermore, we perform rigorous ablation experiments to evaluate different components of our method. Our code is available at: https://github.com/MohsenZand/ObjectBox.

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Acknowledgments

Thanks to Geotab Inc., the City of Kingston, and the Natural Sciences and Engineering Research Council of Canada (NSERC) for their support of this work.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada

    Mohsen Zand, Ali Etemad & Michael Greenspan

Authors
  1. Mohsen Zand
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  2. Ali Etemad
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  3. Michael Greenspan
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Corresponding author

Correspondence to Mohsen Zand .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zand, M., Etemad, A., Greenspan, M. (2022). ObjectBox: From Centers to Boxes for Anchor-Free Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-20080-9_23

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