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Can We Replace Real-World With Synthetic Data in Deep Learning-Based ADAS Algorithm Development?
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DOI：10.1109/MCE.2021.3083206.png)
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Abstract
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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 are being researched more and more. This article tries to answer the question of whether and to what extent synthetically generated data can help in developing the advanced driver-assistance system algorithms for autonomous vehicles, for the object detection task based on deep learning. 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.
Keywords:
Detectors
Training
Data models
Testing
Consumer electronics
Deep learning
Autonomous vehicles
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