This document provides a comparative evaluation of the performance of a deep learning network for different combinations of parameters and hyper-parameters. Although there are numerous studies that report on performance in deep learning networks for ordinary data sets, their performance on small data sets is much less evaluated. The objective of this work is to demonstrate that such a challenging small data set, such as a welding X-ray image data set, can be trained and evaluated obtaining high precision and that it is possible thanks to data augmentation. In fact, this article shows that data augmentation, also a typical technique in any learning process on a large data set, plus that two image channels, such as channels B (blue) and G (green), both are replaced by the Canny edge map and a binary image provided by an adaptive Gaussian threshold, respectively, gives to the network a 3% increase in accuracy, approximately. In summary, the objective of this work is to present the methodology used and the results obtained to estimate the classification accuracy of three main classes of welding defects obtained on a small set of welding X-ray image data.

中文翻译：

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使用深度学习对小型焊缝 X 射线图像数据集进行缺陷分类

本文档提供了对不同参数和超参数组合的深度学习网络性能的比较评估。尽管有许多研究报告了深度学习网络在普通数据集上的性能，但它们在小数据集上的性能评估却少得多。这项工作的目的是证明这样一个具有挑战性的小数据集，例如焊接 X 射线图像数据集，可以训练和评估获得高精度，并且由于数据增强而成为可能。事实上，这篇文章展示了数据增强，也是大数据集上任何学习过程中的典型技术，加上两个图像通道，如通道 B（蓝色）和 G（绿色），都被 Canny 边缘取代地图和自适应高斯阈值提供的二值图像，分别使网络的准确度提高了大约 3%。总之，这项工作的目的是介绍使用的方法和获得的结果，以估计在少量焊接 X 射线图像数据上获得的三类主要焊接缺陷的分类精度。