## 4 Analysis and Discussion
### Datasets and Learning Paradigms
They are also both from the production context in quality assurance applications, where the focus may be not only on recognizing any damages but also on categorizing them in more detail to be able to draw conclusions about process parameters and ultimately improve the quality in the future. The NEU surface dataset is also from the steel industry like the Severstal dataset but with a completely balanced class distribution at six total classes, albeit with only 1800 samples at a similar resolution. GRDDC 2020 and Road Damage Dataset 2018 offer a more nuanced view of road damage with four and eight classes, respectively. Despite their higher number of classes, they are a lot less imbalanced compared to the binary crack segmentation datasets, but this is only partly due to the learning task of object detection being pursued. A clear recommendation can be made for Crack 500 as a crack segmentation benchmark, as it offers a comparatively high balance score, the most data samples for this specific CV task, and the highest-resolution images, as well. This means it is possible to increase the number of samples even further by subdividing them, which may be necessary to reduce the computational cost or use an efficient training batch size, depending on the used model. For crack classification, the Ozgenel crack dataset seems to be recommendable, as it is well balanced and has a tolerable size difference from SDNET. In all other areas, a targeted combination of datasets, such as the NEU surface defect database and Severstal dataset for steel surface detection, to compensate for each other's weaknesses appears to be the best solution. The majority of industrial AVI use cases are based on datasets that are, at most, 10% of the size of the Microsoft common objects in context benchmark dataset (MS COCO) or 1% of the size of the ImageNet benchmark dataset. One would therefore expect that most authors resort to methods that do not require labeled training data or strategies that reduce the number of training samples required. Still, the vast majority of the surveyed literature covers supervised learning applications (97.37%), with only three publications employing unsupervised anomaly detection [48; 50; 125] and one using semi-supervised learning [130]. Regarding the training processes employed, we can identify three groups. The first group, which accounts for 49.32%, utilizes transfer learning; i.e., the models are initialized with weights from one of the major CV challenges, and in most cases, an implementation from one of the many available open-source repositories is also used. This reduces the training time as well as lowers the number of required samples for a sufficiently large training dataset and thus the effort to create such a dataset. The second group, which makes up 30.59% of the publication corpus, trains their models from scratch. This results in a much larger need for training data and training time but is also the only way if the author creates their own architecture, which 21.0% of the authors did. In the third group, which is made up of the remaining 20.09%, there is no indication in the publication of whether the model/s were pretrained or not. In none of the publications using datasets with more than 50,000 samples did the authors utilize transfer learning. This seems to be a viable approach since the datasets are sufficiently large. At the same time, a comparison with transfer learning approaches would be insightful since the underlying models have been pretrained on much larger datasets. However, it is counterintuitive and incomprehensible that transfer learning from benchmarks, or the addition of samples from an open dataset from the same domain, was not used in 20 of the 68 cases with fewer than 1000 samples. Five of the eight classification models in this group performed below the median compared to other models with their respective architectures, while only five of the twelve localization models are in this performance range.