Deep learning models require vast amounts of annotated training data. Gathering
and annotating the data from the real world is an expensive and time-consuming
process. Thus, synthetically generated data is being researched more and more. Two
state-of-the-art deep learning object detectors were trained on various combinations
of real-world and synthetic data. A total of 12 detectors were tested on real-world test
images. Results show that synthetic data can contribute to better detector performance
until a certain ratio between real-world and synthetic data is reached.
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Jelić, Borna; Grbić, Ratko; Vranješ, Mario; Mijić, David
Can we replace real-world with synthetic data in deep learning-based ADAS algorithm
development? // IEEE consumer electronics magazine (2021) doi:10.1109/MCE.2021.3083206
https://ieeexplore.ieee.org/document/9442300
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