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Improving Object Detection in VHR Aerial Orthomosaics

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13807))

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Abstract

In this paper we investigate how to improve object detection on very high resolution orthomosaics. For this, we present a new detection model ResnetYolo, with a Resnet50 backbone and selectable detection heads. Furthermore, we propose two novel techniques to post-process the object detection results: a neighbour based patch NMS algorithm and an IoA based filtering technique. Finally, we fuse color and depth data in order to further increase the results of our deep learning model. We test these improvements on two distinct, challenging use cases: solar panel and swimming pool detection. The images are very high resolution color and elevation orthomosaics, taken from plane photography. Our final models reach an average precision of 78.5% and 44.4% respectively, outperforming the baseline models by over 15% AP.

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Acknowledgements

This project was funded by VLAIO. We would like to thank Vansteelandt BV for preparing and providing the data used in this project.

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Authors and Affiliations

  1. EAVISE - PSI - ESAT, KU Leuven, Leuven, Belgium

    Tanguy Ophoff, Kristof Van Beeck & Toon Goedemé

Authors
  1. Tanguy Ophoff
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  2. Kristof Van Beeck
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  3. Toon Goedemé
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Corresponding author

Correspondence to Tanguy Ophoff .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Ophoff, T., Van Beeck, K., Goedemé, T. (2023). Improving Object Detection in VHR Aerial Orthomosaics. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham. https://doi.org/10.1007/978-3-031-25082-8_18

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  • DOI: https://doi.org/10.1007/978-3-031-25082-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25081-1

  • Online ISBN: 978-3-031-25082-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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