终端条目目标检测在工业应用中的合成训练数据影响研究
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection
AI总结：
本文研究了终端条目目标检测中合成训练数据的影响，通过合成图像与真实数据结合，验证了DINO模型在复杂工业环境中的高效检测性能。
摘要：
在工业制造中，部署深度学习模型进行视觉检测主要受到收集和标注大规模训练数据集的高成本且难以处理的阻碍。尽管从3D CAD模型生成图像是一种常见解决方案，但领域和渲染随机化等个体技术来创建丰富的合成训练数据集在简单领域中已被深入研究。然而，它们在具有密集排列和相似对象的复杂工业任务中的有效性仍不清楚。本文研究了标准目标检测器在终端条目目标检测复杂工业应用中的sim-to-real泛化性能，仔细结合随机化和领域知识。我们逐步描述了创建图像合成管道的过程，该过程在最小的实现努力下实现了高真实感，并解释了这种方法如何转移到其他工业环境。此外，我们创建了一个包含30,000张合成图像和300张人工标注的现实图像的终端条目数据集，该数据集可供参考和未来研究使用。为了提供一个基线作为这些具有挑战性的工业部件检测任务预期性能的下限，我们展示了基于完全合成训练的标准目标检测器在我们数据集上的sim-to-real泛化性能。尽管所有考虑的模型表现相似，但基于变换器的DINO模型在现实测试集上取得了98.40%的平均精度，证明了我们的管道能够从现有的CAD数据中实现高质量的复杂工业环境检测，并且在可控的图像合成努力下。
英文摘要：
In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale training datasets. While image synthesis from 3D CAD models is a common solution, the individual techniques of domain and rendering randomization to create rich synthetic training datasets have been well studied mainly in simple domains. Hence, their effectiveness on complex industrial tasks with densely arranged and similar objects remains unclear. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection, carefully combining randomization and domain knowledge. We describe step-by-step the creation of our image synthesis pipeline that achieves high realism with minimal implementation effort and explain how this approach could be transferred to other industrial settings. Moreover, we created a dataset comprising 30.000 synthetic images and 300 manually annotated real images of terminal strips, which is publicly available for reference and future research. To provide a baseline as a lower bound of the expectable performance in these challenging industrial parts detection tasks, we show the sim-to-real generalization performance of standard object detectors on our dataset based on a fully synthetic training. While all considered models behave similarly, the transformer-based DINO model achieves the best score with 98.40 % mean average precision on the real test set, demonstrating that our pipeline enables high quality detections in complex industrial environments from existing CAD data and with a manageable image synthesis effort.
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