[Submitted on 2 Sep 2026]
Title:Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap
View a PDF of the paper titled Domain generalization and synthetic data in object detection: the enabler, the probe, and the gap, by Elfi I.S. Hofmeijer and 4 other authors
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Abstract:Object detection models often experience performance degradation when deployed under distribution shifts, caused by for example changes in weather type, operational environment, or object appearance. Domain Generalization (DG) aims to develop models that remain robust under such shifts and generalize well to unseen domains. DG research specifically focused on object detection models is scarce, although these models face additional challenges around localization and multi-scale representations. Synthetic data is a promising tool to support in DG, by enabling large-scale generation of diverse new samples. In this paper, we present an object detection-centric review of DG and examine the role of synthetic data from three complementary perspectives. First, synthetic data acts as an enabler of DG through diversification and alignment strategies that aim to improve robustness to distribution shifts. Second, it serves as a probe that enables controlled experimentation to identify and understand failure modes. Third, we discuss the synthetic-to-real gap, a particularly challenging form of domain shift that arises when models trained on synthetic imagery are deployed on real-world data. Through reviewing these perspectives, we identify limitations of current DG approaches for object detection and argue that future research requires representation-aware methods that explicitly address both localization and classification under domain shift.
Comments: Submitted to SPIE Sensors + Imaging 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
ACM classes: I.4
Cite as: arXiv:2610.00030 [cs.CV]
(or arXiv:2610.00030v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.00030
arXiv-issued DOI via DataCite
Submission history
From: Friso G. Heslinga [view email] [v1] Wed, 2 Sep 2026 12:02:02 UTC (1,624 KB)
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