用合成数据训练工业锈蚀检测模型，效果优于传统方法。
SynGen-Vision: Synthetic Data Generation for training industrial vision models
- 结合视觉语言模型与3D渲染生成不同锈蚀场景的合成图像。
- 模型在真实锈蚀图像上测试，mAP50达0.87，性能领先。
- 可定制化扩展至其他工业损伤检测任务。
我们提出一种用于工业磨损检测计算机视觉模型训练的合成数据生成方法。磨损检测是预测性维护中的关键问题，但因缺乏不同磨损场景的数据集，数据准备成本高且耗时。本方法利用视觉语言模型结合3D模拟与渲染引擎，生成多种锈蚀条件的合成数据。通过使用生成数据训练锈蚀检测模型，并在真实工业物体锈蚀图像上测试，结果表明：该方法训练的模型在mAP50指标上达到0.87，优于其他方法。该方案具有可定制性，可便捷扩展至其他工业磨损检测场景。
原文摘要 · Abstract (English)
We propose an approach to generate synthetic data to train computer vision (CV) models for industrial wear and tear detection. Wear and tear detection is an important CV problem for predictive maintenance tasks in any industry. However, data curation for training such models is expensive and time-consuming due to the unavailability of datasets for different wear and tear scenarios. Our approach employs a vision language model along with a 3D simulation and rendering engine to generate synthetic data for varying rust conditions. We evaluate our approach by training a CV model for rust detection using the generated dataset and tested the trained model on real images of rusted industrial objects. The model trained with the synthetic data generated by our approach, outperforms the other approaches with a mAP50 score of 0.87. The approach is customizable and can be easily extended to other industrial wear and tear detection scenarios
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。