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Probabilistic Object Detection via Deep Ensembles

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12540)

Abstract

Probabilistic object detection is the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detection. Measuring uncertainty is important in robotic applications where actions based on erroneous, but high confidence visual detections, can lead to catastrophic consequences. We introduce an approach that employs deep ensembles for estimating predictive uncertainty. The proposed framework achieved 4th place in the ECCV 2020 ACRV Robotic Vision Challenge on Probabilistic Object Detection.

Keywords

  • Object detection
  • Uncertainty estimation
  • Deep ensembles

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Correspondence to William J. Beksi .

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Lyu, Z., Gutierrez, N., Rajguru, A., Beksi, W.J. (2020). Probabilistic Object Detection via Deep Ensembles. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-65414-6_7

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