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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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
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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