Semantic segmentation with inexpensive simulated data

Jukka Peltomäki, Mengyang Chen, Heikki Huttunen

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

Abstrakti

This paper studies the benefits of adding inexpensively gathered simulated data to improve the training of semantic segmentation models. We introduce our implementation to gather simulated datasets with minimal effort. In our implementation, we utilize a commonly available game engine (Unity) and aux-illiary graphical assets to assemble an environment to generate simulated data inexpensively. We also demonstrate that even the usage of spartan simulated data mixed with real-life data can increase the performance of the trained model slightly, given that the ratio of simulated data is suitable for the datasets.
AlkuperäiskieliEnglanti
Otsikko2019 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC)
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)978-1-7281-2769-9
ISBN (painettu)978-1-7281-2770-5
DOI - pysyväislinkit
TilaJulkaistu - lokak. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Nordic Circuits and Systems Conference -
Kesto: 1 tammik. 2000 → …

Conference

ConferenceIEEE Nordic Circuits and Systems Conference
Ajanjakso1/01/00 → …

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