## 3 Categorization of Visual Inspection (Tasks)
### Requirements for Deep-Learning Models in AVI  
Several requirements have to be considered when introducing DL-based AVI to a previously manual inspection process or even to an already automated process that uses \begin{table}
\begin{tabular}{l l} \hline \hline
**Category** & **Search Terms** \\ \hline DL-based computer vision & Deep Learning, Neural Network, Convolutional Neural Network, CNN, Transformer, \\  & Semantic segmentation, Object Detection \\ \hline Automated visual inspection in & Industrial Vision Inspection, Industrial Visual Inspection, Vision Inspection, Visual \\ industrial use cases & Inspection, Damage Detection, Damage Segmentation, Error Detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Search terms used for literature research. classical CV methods. To answer our second question, _\"What are the requirements that have to be considered when applying DL-based models to AVI?\"_, we analyzed our publication corpus with regard to requirements, with either a direct textual mention or indirect mention through evaluation procedures, as well as reported metrics. These requirements can be grouped in two dimensions: on the one hand, between general and application- or domain-specific requirements or, on the other hand, between hard technical and soft human factors. The most general technical challenge is performance, as visualized in Figure 2.  
In the case of the automation of previously manual processes, human-level performance is usually used as a reference value, which is intended to guarantee the same level of safety, as mentioned by [PERSON] et al. for aircraft maintenance [38]. If the target process is already automated, the DL-based solution needs to prevail against the established solution. Performance can be measured by different metrics, as some processes are more focused on false positives (FFs), like the one investigated by [PERSON] et al. [39], or false negatives (FNs). Therefore, it cannot be considered a purely general requirement, as it is affected by the choice of metric. Real-time capability is a strictly technical challenge, as it can be defined by the number of frames per second (FPS) a model can process but is mostly specific, as it is mainly required when inspecting manufactured goods on a conveyor belt or rails/streets from a fast-moving vehicle for maintenance [39; 40; 41; 42]. Hardware constraints are the most specific and rare technical challenge found in our publication corpus. This usually means that the models have to run on a particular edge device, which is limited in memory, the number of floating-point operations per second (FLOPS), or even the possible computational operations it can perform [43]. Sparse (labeled) data are primarily a technical challenge, where the emphasis is put on the fact that models with more parameters generally perform better but require more data samples to optimize those parameters, as well. The labeling process introduces the human factor into this requirement because a consistent understanding of the boundary between different classes is necessary in order to produce a coherent distribution of labels with as few non-application-related inconsistencies or outliers as possible. This is especially true if there are few samples and if multiple different persons create the labels. Models need to perform well with these small labeled industrial datasets [44; 45; 46; 47; 48] or, even better, work without labeled data [49; 50]. One of the key advantages of DL-based models compared to classic CV methods is their generalization capability, which makes them robust against partly hidden objects, changing lighting conditions, or new damage types. This characteristic is required for many use cases where it is not possible to enforce controlled conditions or have full visibility, such as rail track inspection [42; 51; 52], or it is an added benefit when a model is able to extrapolate to previously unseen damages [53].