The analysis setup is illustrated in Fig 1. As a first step, the grayscale high-resolution electron microscope panoramic image is analysed with respect to the grayscale values. As all damage sites considered in this analysis are characterised by a dark area in the micrograph, a suitable cut-off is chosen to identify the potential damage sites. Then a clustering algorithm (DBSCAN [25, 26], implemented in scikit-learn) is used to distinguish actual voids from artefacts like singular pixels below the cut-off value. The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm takes a set of points as input, in our case the micrograph images. Fundamentally, algorithm group points that are close to each other (typically estimated by the Euclidean distance) are integrated into clusters. The algorithm is controlled by two parameters, the distance ε, which effectively describes how far the algorithm should consider points to be part of the current cluster, and the minimum number of points within a distance ε that are needed to form a cluster. Points, which are not associated to clusters are considered as noise. A sample image of 250-by-250 pixels is then taken at each potential damage site from the panoramic micrograph. These candidate pictures are presented to a first deep convolutional neural network, which aims to identify whether the damage site in question is due to an inclusion. If the probability calculated by the neural network exceeds a pre-defined threshold (p1>0.7), this damage site is classified as inclusion, otherwise, the image is cropped to 100-by-100 pixels and presented to a second deep convolutional neural network which is specifically trained to classify a damage site into martensite cracking (MC), notch (N), grain boundary decohesion (B) or phase boundary decohesion (PB). Again, if the calculated probability exceeds a given threshold (p2>0.7), the damage site is classified accordingly, otherwise the picture is flagged for later manual analysis. Finally, the original panoramic microscope image is amended such that all identified damage sites are highlighted and labelled.
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