论文标题
黑暗中的光：工业计算机视觉的深度学习实践
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
论文作者
论文摘要
近年来，大型预训练的深度神经网络（DNN）彻底改变了计算机视觉领域（CV）。尽管这些DNN已被证明非常适合一般图像识别任务，但在行业中的应用通常是由于三个原因而排除的：1）大型预培训的DNN建立在数亿个数百万个参数上，使许多设备上的部署不可能，使得基础的数据集在构造的范围中，同时构成一般物体，同时构成了构成特定的物体，而构成了构成的构成，构成了构成的构成，构成了构成的构成，构成了构成的构成，构成了构成的构成，构成了构成的构成，构成了构成的构成，构成了构成的构成）受偏见的预培训的DNN为公司提出了法律问题。作为一种补救措施，我们研究了我们从头开始训练的简历的神经网络。为此，我们使用了太阳能晶圆制造商的真实情况。我们发现，我们的神经网络具有与预训练的DNN相似的性能，即使它们由参数少得多，并且不依赖第三方数据集。
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.
