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Improving Deep Object Detection Backbone with Feature Layers

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12746))

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Abstract

Deep neural networks are the frontier in object detection, a key modern computing task. The dominant methods involve two-stage deep networks that heavily rely on features extracted by the backbone in the first stage. In this study, we propose an improved model, ResNeXt101S, to improve feature quality for layers that might be too deep. It introduces splits in middle layers for feature extraction and a deep feature pyramid network (DFPN) for feature aggregation. This backbone is neither much larger than the leading model ResNeXt nor increasing computational complexity distinctly. It is applicable to a range of different image resolutions. The evaluation of customized benchmark datasets using various image resolutions shows that the improvement is effective and consistent. In addition, the study shows input resolution does impact detection performance. In short, our proposed backbone can achieve better accuracy under different resolutions comparing to state-of-the-art models.

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Notes

  1. 1.

    Available on the MS COCO dataset website http://cocodataset.org.

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Author information

Authors and Affiliations

  1. RMIT University, Melbourne, VIC, 3000, Australia

    Weiheng Hong & Andy Song

  2. Xiamen Research Center of Urban Planning Digital Technology, Xiamen, 361015, Fujian, China

    Weiheng Hong

Authors
  1. Weiheng Hong
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  2. Andy Song
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Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Hong, W., Song, A. (2021). Improving Deep Object Detection Backbone with Feature Layers. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-77977-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77976-4

  • Online ISBN: 978-3-030-77977-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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