In the aerospace industry, automated fibre laying processes are often applied for economical composite part fabrication. Unfortunately, the current mandatory visual quality assurance process takes up to 50% of the entire manufacturing time. An automised classification of manufacturing deviations using Neural Networks potentially improves the inspection's effectiveness. Unfortunately, the automated decision-making procedures of machine learning approaches are challenging to trace. Therefore, we introduce an approach for evaluating the classifiers response for this use case.

For this purpose, we present a parallel classification approach of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) with suitable intermediate checking stages between both classification processes. The particular novelty of this study is this intermediate comparison to trace the behaviour of the two classifiers along their image processing chains and to project the results back to the input image.

With respect to the SVM, we analyse their extracted input features via t-Distributed Stochastic Neighbor Embedding calculations and parallel coordinates plots. Moreover, the classification score of the SVM as well as the feature vector distances within the SVM are investigated. For the CNN, the outputs of its first joined convolutional layer are correlated with the raw input images of different classes using Structural Similarity Index Measure metrics. Additionally, also the CNN's classification rates are analysed. Accordingly, a suitable uncertainty confidence interval for the CNN is determined on the bases of its neural activations. Finally, the relevance of individual pixels for the CNN decision is determined through Smooth Integrated Gradients and linked to the manually extracted image features for the SVM Classifier.

The results of this paper are particularly valuable for developers and users of visual inspection systems in safety-critical domains.

在航空航天工业中，自动化纤维铺设工艺通常用于经济的复合材料部件制造。不幸的是，目前强制性的视觉质量保证过程占用了整个制造时间的 50%。使用神经网络对制造偏差进行自动分类可能会提高检查的有效性。不幸的是，机器学习方法的自动化决策过程很难追踪。因此，我们引入了一种评估该用例的分类器响应的方法。

为此，我们提出了卷积神经网络(CNN) 和支持向量机(SVM)的并行分类方法，在两个分类过程之间具有合适的中间检查阶段。这项研究的特别新颖之处在于这种中间比较，以跟踪两个分类器沿其图像处理链的行为并将结果投影回输入图像。

关于 SVM，我们通过t-Distributed Stochastic Neighbor Embedding计算和平行坐标图分析它们提取的输入特征。此外，研究了 SVM 的分类分数以及 SVM 内的特征向量距离。对于 CNN，其第一个连接卷积层的输出使用结构相似性指数度量指标与不同类别的原始输入图像相关联。此外，还分析了 CNN 的分类率。因此，CNN 的合适的不确定性置信区间是根据其神经激活确定的。最后，单个像素与 CNN 决策的相关性是通过以下方式确定的平滑集成梯度并链接到 SVM 分类器的手动提取的图像特征。

本文的结果对于安全关键领域的视觉检测系统的开发人员和用户特别有价值。