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An Assessment of Self-supervised Learning for Data Efficient Potato Instance Segmentation

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Towards Autonomous Robotic Systems (TAROS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14136))

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Abstract

This work examines the viability of self-supervised learning approaches in the field of agri-robotics, specifically focusing on the segmentation of densely packed potato tubers in storage. The work assesses the impact of both the quantity and quality of data on self-supervised training, employing a limited set of both annotated and unannotated data. Mask R-CNN with a ResNet50 backbone is used for instance segmentation to evaluate self-supervised training performance. The results indicate that the self-supervised methods employed have a modest yet beneficial impact on the downstream task. A simpler approach yields more effective results with a larger dataset, whereas a more intricate method shows superior performance with a refined, smaller self-supervised dataset.

This work was supported by the Engineering and Physical Sciences Research Council [EP/S023917/1] as part of the AgriFoRwArdS CDT.

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Authors and Affiliations

  1. University of Lincoln, Lincoln, UK

    Bradley Hurst, Nicola Bellotto & Petra Bosilj

  2. Università di Padova, Padua, Italy

    Nicola Bellotto

Authors
  1. Bradley Hurst
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  2. Nicola Bellotto
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  3. Petra Bosilj
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Correspondence to Bradley Hurst .

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Editors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Fumiya Iida

  2. University of Oxford, Oxford, UK

    Perla Maiolino

  3. University of Cambridge, Cambridge, UK

    Arsen Abdulali

  4. Brunel University London, Uxbridge, UK

    Mingfeng Wang

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Hurst, B., Bellotto, N., Bosilj, P. (2023). An Assessment of Self-supervised Learning for Data Efficient Potato Instance Segmentation. In: Iida, F., Maiolino, P., Abdulali, A., Wang, M. (eds) Towards Autonomous Robotic Systems. TAROS 2023. Lecture Notes in Computer Science(), vol 14136. Springer, Cham. https://doi.org/10.1007/978-3-031-43360-3_22

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  • DOI: https://doi.org/10.1007/978-3-031-43360-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43359-7

  • Online ISBN: 978-3-031-43360-3

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