论文标题
工业应用中对象分类的合成数据
Synthetic Data for Object Classification in Industrial Applications
论文作者
论文摘要
机器学习中最大的挑战之一是数据收集。培训数据是重要的部分，因为它决定了模型的行为。在对象分类中，每个对象捕获大量图像并不总是可能的，并且可能非常耗时且乏味。因此，这项工作探讨了使用游戏引擎来应对培训数据集中有限数据的创建人造图像。我们将真实和合成数据结合起来训练对象分类引擎，该策略已证明有益于增加对分类器做出的决策的信心，这在工业设置中通常至关重要。为了结合实际和合成数据，我们首先在大量合成数据上训练分类器，然后在真实图像上对其进行微调。另一个重要的结果是，微调所需的真实图像数量不是很高，每班只有12或24张图像达到顶部精度。这大大减少了捕获大量实际数据的要求。
One of the biggest challenges in machine learning is data collection. Training data is an important part since it determines how the model will behave. In object classification, capturing a large number of images per object and in different conditions is not always possible and can be very time-consuming and tedious. Accordingly, this work explores the creation of artificial images using a game engine to cope with limited data in the training dataset. We combine real and synthetic data to train the object classification engine, a strategy that has shown to be beneficial to increase confidence in the decisions made by the classifier, which is often critical in industrial setups. To combine real and synthetic data, we first train the classifier on a massive amount of synthetic data, and then we fine-tune it on real images. Another important result is that the amount of real images needed for fine-tuning is not very high, reaching top accuracy with just 12 or 24 images per class. This substantially reduces the requirements of capturing a great amount of real data.
