In recent years, large pre-trained deep neural networks (DNNs) have
revolutionized the field of computer vision (CV). Although these DNNs have been
shown to be very well suited for general image recognition tasks, application
in industry is often precluded for three reasons: 1) large pre-trained DNNs are
built on hundreds of millions of parameters, making deployment on many devices
impossible, 2) the underlying dataset for pre-training consists of general
objects, while industrial cases often consist of very specific objects, such as
structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal
issues for companies. As a remedy, we study neural networks for CV that we
train from scratch. For this purpose, we use a real-world case from a solar
wafer manufacturer. We find that our neural networks achieve similar
performances as pre-trained DNNs, even though they consist of far fewer
parameters and do not rely on third-party datasets.

许多设备无法部署，普通对象的预训练数据集，以及潜在的偏见问题限制了大型预训练深度神经网络用于工业应用。本研究从太阳能电池片制造商的真实案例出发，构建了一个从零开始训练的神经网络，其参数更少、不依赖于第三方数据集，但性能与预训练深度神经网络相似。