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BBRefinement: an universal scheme to improve precision of box object detectors

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F22%3AA23026BN" target="_blank" >RIV/61988987:17610/22:A23026BN - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/2076-3417/12/7/3402/htm" target="_blank" >https://www.mdpi.com/2076-3417/12/7/3402/htm</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/app12073402" target="_blank" >10.3390/app12073402</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    BBRefinement: an universal scheme to improve precision of box object detectors

  • Original language description

    We present a conceptually simple yet powerful and general scheme for refining the predictions of bounding boxes produced by an arbitrary object detector. Our approach was trained separately on single objects extracted from ground truth labels. For inference, it can be coupled with an arbitrary object detector to improve its precision. The method, called BBRefinement, uses a mixture of data consisting of the image crop of an object and the object’s class and center. Because BBRefinement works in a restricted domain, it does not have to be concerned with multiscale detection, recognition of the object’s class, computing confidence, or multiple detections. Thus, the training is much more effective. It results in the ability to improve the performance of SOTA architectures by up to two mAP points on the COCO dataset in the benchmark. The refinement process is fast; it adds 50–80 ms overhead to a standard detector using RTX2080; therefore, it can run in real time on standard hardware. Finally, we show that BBRefinement can also be applied to COCO’s ground truth labels to create new, more precise labels. The link to the source code is provided in the contribution.

  • 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

    <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

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Applied Sciences

  • ISSN

    2076-3417

  • e-ISSN

    2076-3417

  • Volume of the periodical

  • Issue of the periodical within the volume

    7

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    12

  • Pages from-to

    1-12

  • UT code for WoS article

    000781328100001

  • EID of the result in the Scopus database

    2-s2.0-85127765151