## 5 Conclusions  
In this review, we provide a comprehensive overview of 196 scientific publications published between 2012 and March 2023 dealing with the application of DL-based models for AVI in manufacturing and maintenance. The publications were categorized and critically evaluated based on six guiding questions, which helped us throughout the literature review. By answering these questions, we can report several findings. Based on the literature, we derived key requirements for AVI use cases. These requirements were subsequently organized into two distinct dimensions, namely, general versus specific requirements and hard technical versus soft human factors. As a result, we identified several essential aspects that transcend particular use cases and are of broad significance. These encompass performance, explainability, robustness/generalization, the ability to handle sparse data, real-time capability, and adherence to hardware constraints. The use cases in which DL-based AVI is applied can be structured hierarchically. All of them fall under quality inspection, but they can be subdivided into damage detection, completeness check, and others, where the latest contains use cases like plant disease detection, food contamination detection, and indirect wear and vibration estimation. The damage detection category features one additional sub-use-case that occurs frequently: crack detection. We analyzed the datasets utilized in AVI regarding size distributions as well as the uniformity of their class distributions. This is measured by a balance metric that we propose. In many cases, the dataset parameters required to compute it are incompletely or imprecisely reported. On the one hand, this limited our analysis, but on the other hand, it also makes the results less comparable between publications and harder to reproduce and hinders further development of existing research results by others, which is detrimental to the progress of AVI overall. In addition, we could identify 14 datasets that were utilized more than once to benchmark tasks, out of the wide selection of 77 open datasets provided by authors. So, we want to encourage AVI researchers to give more detailed dataset descriptions to improve comparability and use and improve open datasets to advance the field as a whole. We found that AVI can be addressed with classification and localization tasks, of which both come in two forms: binary and multi-class. These four AVI tasks are closely related to the three most common CV tasks, which means that classification AVI tasks are performed by image classification models, while localization tasks are performed by either object detection or segmentation models. In the domain of maintenance-oriented AVI use cases, we observed that localization methodologies are prevalent, as they are utilized approximately 77.2% of the time, while classification methods are employed in about 22.8% of instances. In contrast, within the manufacturing-focused AVI applications, the distribution of localization and classification strategies is more evenly balanced, with localization accounting for 49.2% and classification accounting for 50.8% of use cases. These discernible discrepancies in the adoption of localization and classification approaches can be attributed to various environmental factors that govern the distinct operational requirements and objectives of maintenance and manufacturing scenarios. Especially for maintenance, we recommend prioritizing localization-capable methodologies such as YOLO and Mask RCNNs. The inherent capability of these methods to precisely localize and delineate objects of interest is deemed crucial to enhancing the effectiveness of AVI for maintenance purposes. If the task can be performed through classification, ResNet and EfficientNet are the models we recommend using. We also compared the SOTA in AVI applications with the advance in academic CV research. We identified a time gap of two to three years between the first proposal of a model and its transfer to an industrial AVI application.