Schraml Dominik, Notni Gunther
Group for Quality Assurance and Industrial Image Processing, Ilmenau University of Technology, 98639 Ilmenau, Germany.
Steinbeis Qualitätssicherung und Bildverarbeitung GmbH, 98693 Ilmenau, Germany.
Sensors (Basel). 2024 Jan 19;24(2):649. doi: 10.3390/s24020649.
Industrial-quality inspections, particularly those leveraging AI, require significant amounts of training data. In fields like injection molding, producing a multitude of defective parts for such data poses environmental and financial challenges. Synthetic training data emerge as a potential solution to address these concerns. Although the creation of realistic synthetic 2D images from 3D models of injection-molded parts involves numerous rendering parameters, the current literature on the generation and application of synthetic data in industrial-quality inspection scarcely addresses the impact of these parameters on AI efficacy. In this study, we delve into some of these key parameters, such as camera position, lighting, and computational noise, to gauge their effect on AI performance. By utilizing Blender software, we procedurally introduced the "flash" defect on a 3D model sourced from a CAD file of an injection-molded part. Subsequently, with Blender's Cycles rendering engine, we produced datasets for each parameter variation. These datasets were then used to train a pre-trained EfficientNet-V2 for the binary classification of the "flash" defect. Our results indicate that while noise is less critical, using a range of noise levels in training can benefit model adaptability and efficiency. Variability in camera positioning and lighting conditions was found to be more significant, enhancing model performance even when real-world conditions mirror the controlled synthetic environment. These findings suggest that incorporating diverse lighting and camera dynamics is beneficial for AI applications, regardless of the consistency in real-world operational settings.
工业质量检测，尤其是那些利用人工智能的检测，需要大量的训练数据。在注塑成型等领域，为获取此类数据而生产大量有缺陷的零件会带来环境和财务方面的挑战。合成训练数据成为解决这些问题的一种潜在解决方案。尽管从注塑零件的3D模型创建逼真的合成2D图像涉及众多渲染参数，但当前关于工业质量检测中合成数据的生成和应用的文献几乎没有涉及这些参数对人工智能效能的影响。在本研究中，我们深入研究了一些关键参数，如相机位置、光照和计算噪声，以评估它们对人工智能性能的影响。通过使用Blender软件，我们在一个从注塑零件的CAD文件获取的3D模型上程序式地引入了“飞边”缺陷。随后，利用Blender的Cycles渲染引擎，我们为每个参数变化生成了数据集。然后，这些数据集被用于训练一个预训练的EfficientNet-V2，用于对“飞边”缺陷进行二元分类。我们的结果表明，虽然噪声的影响较小，但在训练中使用一系列噪声水平可以提高模型的适应性和效率。发现相机位置和光照条件的变化更为显著，即使现实世界条件与受控的合成环境相似，也能提高模型性能。这些发现表明，纳入多样化的光照和相机动态变化对人工智能应用有益，无论现实世界操作设置中的一致性如何。
