Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models

• URL: http://arxiv.org/abs/2305.14621v1
• Date: Wed, 24 May 2023 01:39:41 GMT
• Title: Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models
• Authors: Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg, Yunpeng Liu, Matthew Niedoba, Xiaoxuan Liang, Dylan Green, Justice Sefas, Jonathan Wilder Lavington, Frank Wood, Adam Scibior
• Abstract summary: It is important to make the distribution of synthetic data as close as possible to the distribution of real data. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.
• Score: 14.547359434855695
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.

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