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Label-free retraining for improved ground plane segmentation

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Abstract

Due to increased potential applications of unmanned aerial vehicles over urban areas, algorithms for the safe landing of these devices have become more critical. One way to ensure a safe landing is to locate the ground plane regions of images captured by the device camera that are free of obstacles by deep semantic segmentation networks. In this paper, we study the performance of semantic segmentation networks trained for this purpose at a particular altitude and location. We show that a variation in altitude and location significantly decreases network performance. We then propose an approach to retrain the network using only a new set of images and without marking the ground regions in this novel training set. Our experiments show that we can convert a network’s operating range from low to high altitudes and vice versa by label-free retraining.

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Data availability

UAVid is available online at https://uavid.nl, SDrone is available at http://dronedataset.icg.tugraz.at, and the annotations used in this study are available at reasonable request from the first author.

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Authors and Affiliations

  1. Department of Computer Engineering, İzmir Institute of Technology, 35430, İzmir, Turkey

    Furkan Eren Uzyıldırım & Mustafa Özuysal

Authors
  1. Furkan Eren Uzyıldırım
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  2. Mustafa Özuysal
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Contributions

All authors contributed to the conceptualization and design. FEU prepared the datasets, implemented the approach, and conducted the experiments. MÖ prepared the draft manuscript. All authors revised the manuscript.

Corresponding author

Correspondence to Mustafa Özuysal.

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The code is available at reasonable request from the first author.

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Uzyıldırım, F.E., Özuysal, M. Label-free retraining for improved ground plane segmentation. SIViP 17, 2465–2471 (2023). https://doi.org/10.1007/s11760-022-02463-1

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