A pipeline for detecting and classifying objects in images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA21025BL" target="_blank" >RIV/61988987:17610/20:A21025BL - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9204121" target="_blank" >https://ieeexplore.ieee.org/document/9204121</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/DSMP47368.2020.9204121" target="_blank" >10.1109/DSMP47368.2020.9204121</a>

Result language
angličtina
Original language name
A pipeline for detecting and classifying objects in images
Original language description
With the increased accessibility to neural network frameworks and computation clouds, a wide range of competition websites offer real tasks for the neural network community to join. In this paper, we discuss a problem of object detection and classification. Based on our experience, we describe a general pipeline and necessary steps that may help researchers willing to participate in such competition. We partition the problem into two separate tasks. Firstly, we present state of the art for relevant neural networks concerning accuracy and computation time trade-off. Further, we create a survey of major techniques that leads to accuracy improvement. Namely, we recall image augmentation techniques, demonstrate the impact of various optimizers, and discuss ensemble techniques. The pipeline and techniques reflect our experience with a competition, in which we were able to reach a highly competitive solution and ended in fourth place. The uniqueness of our solution is that we used only free Google Colab computation service and still overperformed many more computation extensive approaches.
Czech name
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Czech description
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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)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of IEEE Third International Conference Data Stream Mining & Processing 2020
ISBN
978-1-7281-3214-3
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
163-168
Publisher name
IEEE
Place of publication
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Event location
Lvov, Ukrajina
Event date
Aug 21, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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