Semantically-Guided Domain Randomization for Industrial Object Detection in Low-Image-Budget Regimes

2026-09-22

AI Summary

研究通过语义引导的领域随机化方法，利用少量未标注数据生成合成图像，以提高低图像预算下的工业物体检测性能。

Abstract

Retraining visual perception pipelines in High-Mix, Low-Volume (HMLV) automotive manufacturing must be carried out under tight annotation, energy, and time budgets, yet most Synthetic Data Generation (SDG) strategies still operate in the thousands of images. This work evaluates Semantically-Guided Domain Randomization (S-GDR), an annotation-free adaptation pipeline that couples Vision-Language Model (VLM)-based semantic captioning of a small unannotated real reference set with diffusion-based background synthesis (Stable Diffusion XL (SDXL) conditioned by ControlNet and IP-Adapter) and mask-based object composition. On an automotive multi-object detection benchmark and with a fixed budget of 200 synthetic training images, S-GDR reaches mAP50-95 = 0.739 on a real held-out test set, outperforming a domain-randomized render baseline (mAP50-95 = 0.697) as well as brightness filtering, perceptual hashing, CycleGAN style transfer, and unguided diffusion variants sharing the same 200-image budget. These initial observations position S-GDR as a promising annotation- free alternative for extreme data-scarcity regimes.

Problem

• Synthetic Data Generation
• Domain Randomization
• Low-Image-Budget
• Industrial Object Detection
• Vision-Language Model

Innovation

• Semantically-Guided Domain Randomization
• Vision-Language Model
• Synthetic Data Generation
• Data-Scarcity Regimes