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Stały URI dla kolekcjihttp://hdl.handle.net/11652/4775

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20232

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Autorsearch.filters.applied.operator.equals: search.filters.author.Majek, KarolTematsearch.filters.applied.operator.equals: search.filters.subject.semantic segmentation

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Teraz wyświetlane 1 - 2 z 2
  • Pozycja
    Semantic Segmentation for Autonomous Drone Delivery SUADD’23 Challenge
    (Wydawnictwo Politechniki Łódzkiej, 2023) Mrukwa, Anna; Majek, Karol
    The popularity of drones as well as other different flying devices remains undeterred for several years now, with various industries recognizing their usefulness in a range of applications. However, the effectiveness of such systems is heavily dependent on real-time autonomous surface identification. The goal of this work is to evaluate recently published dataset dedicated to Unmanned Aircraft Systems. We performed experiments using several semantic segmentation neural network architectures. We outline possible improvements for future research and promising results for attentionbased solutions in the field.
  • Pozycja
    BDOT10k-seg: A Dataset for Semantic Segmentation
    (Wydawnictwo Politechniki Łódzkiej, 2023) Kos, Aleksandra; Majek, Karol
    In this work, we describe BDOT10k-seg, a novel aerial dataset for semantic and instance segmentation. Our data covers almost the entire territory of Poland (314,000 km2) and provides precise pixel-level annotations for 286 classes of topographical objects, including buildings, roads, rivers, lakes, airports, agricultural areas, and forests. BDOT10-seg consists of 60,718 images with a resolution of 3 to 75 centimeters per pixel, and more than 40 million object instances. The average image size is 12,367 px because, unlike other publicly available datasets, we do not modify the source orthoimages. The code for generating the BDOT10k-seg dataset is publicly available.