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YOLO-ASC: You Only Look Once And See Contours

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA21021UC" target="_blank" >RIV/61988987:17610/20:A21021UC - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9207223" target="_blank" >https://ieeexplore.ieee.org/document/9207223</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/IJCNN48605.2020.9207223" target="_blank" >10.1109/IJCNN48605.2020.9207223</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    YOLO-ASC: You Only Look Once And See Contours

  • Original language description

    YOLO is a useful, one-stage tool for object detection and classification. In this paper, we consider the application of grocery product detection. The grocery stores have a significant amount of product classes, so it is beneficial to postpone the classification into a second, specialized neural network with a higher capacity. Extracting bounding boxes for a classification network is not precise enough as the detected area includes redundant information about the background. We propose YOLO-ASC, which, for rectangular-based objects, detects bounding boxes together with object contour using a quadrangular. This approach allows detecting objects more accurately and without the background. For the quadrangular detection functionality, YOLO-ASC shares the feature maps that are already present in the network, and therefore its inference time is almost identical to the original YOLO. YOLO reaches high detection precision by using YOLO apriori knowledge, anchors extracted from data. In this work, we present two experiments where we demonstrate that YOLO-ASC training converges faster due to the symbiosis between the bounding box detection and quadrangular detection. Finally, we propose a tool for generating synthetic datasets with quadrangular labels that is helpful for transfer learning.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

    <a href="/en/project/EF17_049%2F0008414" target="_blank" >EF17_049/0008414: Centre for the development of Artificial Intelligence Methods for the Automotive Industry of the region</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    2020 International Joint Conference on Neural Networks (IJCNN)

  • ISBN

    978-1-7281-6926-2

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    1-7

  • Publisher name

    IEEE

  • Place of publication

    USA

  • Event location

    Glasgow , United Kingdom

  • Event date

    Jul 19, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article