## 2 Methodology of Literature Research  
In our work, we analyzed the SOTA by surveying recent papers solving AVI use cases with DL-based approaches. Figure 1 succinctly illustrates the employed literature research methodology for our comprehensive review. Therefore, we followed the systematic literature research procedure proposed by [PERSON] et al. [11] and adhered to their recommended process, starting with the investigation of survey papers on VI with DL to justify the necessity of our research. We used scholarly search engines like Google Scholar, Web of Science (WOS), and Semantic Scholar and found many related survey publications on VI with DL. However, all publications put their focus on DL-based VI in specific industry sectors or methodologies but not on industrial VI as a whole. [PERSON] et al. [12], e.g., focused their survey on surface defect detection with DL techniques. The same applies to the surveys by [PERSON] et al. [13], [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18], who concentrated on different industry sectors, like steel production, railway applications, power lines, or manufacturing in general. There are also many publications in the areas of civil engineering and structural health monitoring. [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22] studied DL-based approaches to crack detection. [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25] reviewed the general SOTA in the AVI of pavements and roads, while [PERSON] et al. [26] focused more specifically on the detection of potholes. A wider scope is covered by [PERSON] et al. [27] and [PERSON] et al. [28], as they consider structural health monitoring as a whole, not just cracks or pavements and roads. [PERSON] et al. [29] highlighted the consideration of falling facade objects in their review, while [PERSON] et al. [30] emphasized the application of generative adversarial networks (GANs). [PERSON] et al. [31] surveyed the literature on class imbalance for VI. [PERSON] et al. [32] covered a wide range of industrial applications but restricted their survey to unsupervised anomaly detection. Similarly, [PERSON] et al. [33] reviewed the literature with a focus on anomaly detection with DL in AVI. In addition to an overview, they also discuss the advantages and disadvantages of the reviewed anomaly detection approaches. The only survey we found that deals with VI as a whole, i.e., not only one VI use case or industry sector, was written in 1995 by [PERSON] et al. [34]. However, this work was published over 20 years ago and did not deal with DL and, therefore, does not cover new challenges that emerged with the era of digitization. Given the lack of a domain-overarching perspective on VI, we gathered relevant publications utilizing the WOS online research tool, as it is recognized as the leading scientific citation and analytical platform and lists publications across a wide area of knowledge domains [35]. The research field we are interested in is an intersection of two topics: DL-based CV and AVI in industrial use cases. Thus, we gathered search terms with the intention of covering the most important aspects of each of the two areas. Table 1 lists all defined search terms. We formulated search queries to find publications that contain at least one term from each of the two categories and conducted our literature search on 4 March 2023. Figure 1: Flow chart of the literature research process. Furthermore, we defined additional constraints to refine the results of the query. The considered time range started from 1 January 2010 in order to also include less high-profile publications before the first largely successful CV model \"AlexNet\" was proposed in 2012. Furthermore, we only considered open-access publications written in English to be transparent and comprehensible in our work by only using references accessible to the scientific community. The query with the aforementioned constraints resulted in 6583 publications.