Improving object detector training on synthetic data by starting with a strong baseline methodology

Frank A. Ruis; Alma M. Liezenga; Friso G. Heslinga; Luca Ballan; Thijs A. Eker; Richard J. M. den Hollander; Martin C. van Leeuwen; Judith Dijk; Wyke Huizinga

doi:10.1117/12.3013441

7 June 2024 Improving object detector training on synthetic data by starting with a strong baseline methodology

Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga, Luca Ballan, Thijs A. Eker, Richard J. M. den Hollander, Martin C. van Leeuwen, Judith Dijk, Wyke Huizinga

Author Affiliations +

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 1303511 (2024) https://doi.org/10.1117/12.3013441
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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 (CNN) models that have been shown to perform well when trained on real data, but have limited ability to perform well when trained on synthetic data. For example, some architectures allow for fine-tuning with the expectation of large quantities of training data and are prone to overfitting on synthetic data. Related work usually ignores various best practices from object detection on real data, e.g. by training on synthetic data from a single environment with relatively little variation. In this paper we propose a methodology for improving the performance of a pre-trained object detector when training on synthetic data. Our approach focuses on extracting the salient information from synthetic data without forgetting useful features learned from pre-training on real images. Based on the state of the art, we incorporate data augmentation methods and a Transformer backbone. Besides reaching relatively strong performance without any specialized synthetic data transfer methods, we show that our methods improve the state of the art on synthetic data trained object detection for the RarePlanes and DGTA-VisDrone datasets, and reach near-perfect performance on an in-house vehicle detection dataset.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga, Luca Ballan, Thijs A. Eker, Richard J. M. den Hollander, Martin C. van Leeuwen, Judith Dijk, and Wyke Huizinga "Improving object detector training on synthetic data by starting with a strong baseline methodology", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 1303511 (7 June 2024); https://doi.org/10.1117/12.3013441

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