Improving object detector training on synthetic data by starting with a strong baseline methodology
Frank A. Ruis,
Alma Liezenga,
Friso G. Heslinga
et al.
Abstract:Collecting and annotating real-world data for the development of object detection models is a time-consuming and expensive process. In the military domain in particular, data collection can also be dangerous or infeasible. Training models on synthetic data may provide a solution for cases where access to real-world training data is restricted. However, bridging the reality gap between synthetic and real data remains a challenge. Existing methods usually build on top of baseline Convolutional Neural Network (CN… Show more
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