用合成数据提升机器人在油田等危险环境下的物体检测能力。
A Synthetic Dataset for Manometry Recognition in Robotic Applications
- 结合程序化渲染与AI视频生成，构建逼真合成数据集。
- 真实与合成数据1:1混合训练，检测准确率最高。
- 适合高危工业场景下低成本开发感知系统的研究者。
本文针对海上油田等复杂工业环境中物体检测模型训练面临的数据稀缺和采集成本高的问题。这些高危场景下的数据收集限制了自主巡检系统的发展。为此，我们提出一种混合数据合成流程，结合BlenderProc的程序化渲染与NVIDIA Cosmos-Predict2的AI视频生成技术。前者生成带领域随机化的照片级图像，后者生成具有时间变化的物理一致视频序列。基于真实与合成数据混合的YOLO检测器，在1:1比例时达到最高准确率，优于仅使用真实数据训练的模型。结果表明，合成数据生成是安全、经济且可靠的工业感知系统开发策略。
原文摘要 · Abstract (English)
This paper addresses the challenges of data scarcity and high acquisition costs in training robust object detection models for complex industrial environments, such as offshore oil platforms. Data collection in these hazardous settings often limits the development of autonomous inspection systems. To mitigate this issue, we propose a hybrid data synthesis pipeline that integrates procedural rendering and AI-driven video generation. The approach uses BlenderProc to produce photorealistic images with domain randomization and NVIDIA's Cosmos-Predict2 to generate physically consistent video sequences with temporal variation. A YOLO-based detector trained on a composite dataset, combining real and synthetic data, outperformed models trained solely on real images. A 1:1 ratio between real and synthetic samples achieved the highest accuracy. The results demonstrate that synthetic data generation is a viable, cost-effective, and safe strategy for developing reliable perception systems in safety-critical and resource-constrained industrial applications.
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