Cross-evaluation of a parallel operating SVM – CNN classifier for reliable internal decision-making processes in composite inspection

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.