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Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA22025BJ" target="_blank" >RIV/61988987:17610/21:A22025BJ - isvavai.cz</a>

  • Result on the web

    <a href="https://link.springer.com/article/10.1007/s00521-021-05978-9" target="_blank" >https://link.springer.com/article/10.1007/s00521-021-05978-9</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00521-021-05978-9" target="_blank" >10.1007/s00521-021-05978-9</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3

  • Original language description

    We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO. Poly-YOLO builds on the original ideas of YOLOv3 and removes two of its weaknesses: a large amount of rewritten labels and an inefficient distribution of anchors. Poly-YOLO reduces the issues by aggregating features from a light SE-Dark-net-53 backbone with a hypercolumn technique, using stairstep upsampling, and produces a single scale output with high resolution. In comparison with YOLOv3, Poly-YOLO has only 60% of its trainable parameters but improves the mean average precision by a relative 40%. We also present Poly-YOLO lite with fewer parameters and a lower output resolution. It has the same precision as YOLOv3, but it is three times smaller and twice as fast, thus suitable for embedded devices. Finally, Poly-YOLO performs instance segmentation by bounding polygons. The network is trained to detect size-independent polygons defined on a polar grid. Vertices of each polygon are being predicted with their confidence, and therefore Poly-YOLO produces polygons with a varying number of vertices. Source code is available at https://gitlab.com/irafm-ai/poly-yolo.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    NEURAL COMPUT APPL

  • ISSN

    0941-0643

  • e-ISSN

    1433-3058

  • Volume of the periodical

  • Issue of the periodical within the volume

    February

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    16

  • Pages from-to

  • UT code for WoS article

    000758302200004

  • EID of the result in the Scopus database

    2-s2.0-85124764403