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Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

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Abstract

In this paper, we employ anomaly detection methods to enhance the ability of object detectors by using the context of their detections. This has numerous potential applications from boosting the performance of standard object detectors, to the preliminary validation of annotation quality, and even for robotic exploration and object search. We build our method on autoencoder networks for detecting anomalies, where we do not try to filter incoming data based on anomality score as is usual, but instead, we focus on the individual features of the data representing an actual scene. We show that one can teach autoencoders about the contextual relationship of objects in images, i.e. the likelihood of co-detecting classes in the same scene. This can then be used to identify detections that do and do not fit with the rest of the current observations in the scene. We show that the use of this information yields better results than using traditional thresholding when deciding if weaker detections are actually classed as observed or not. The experiments performed not only show that our method significantly improves the performance of CNN object detectors, but that it can be used as an efficient tool to discover incorrectly-annotated images.

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Acknoledgement

The authors acknowledge the support of the Czech Science Foundation project “Towards long-term autonomy through introduction of the temporal domain into spatial representations used in robotics “20-27034J”. The calculations were performed using computational resources provided by the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”.

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Authors and Affiliations

  1. Artificial Intelligence Center, FEE CTU, Prague, Czech Republic

    Jan Blaha, George Broughton & Tomáš Krajník

Authors
  1. Jan Blaha
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  2. George Broughton
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  3. Tomáš Krajník
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Corresponding author

Correspondence to Jan Blaha .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Blaha, J., Broughton, G., Krajník, T. (2021). Boosting the Performance of Object Detection CNNs with Context-Based Anomaly Detection. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-67537-0_11

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  • Online ISBN: 978-3-030-67537-0

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