## 3 Categorization of Visual Inspection (Tasks)
### Overview on How to Solve Automated Visual Inspection with Deep-Learning Models  
In the following, we aim to answer our third guiding question, _\"Are there certain recurring AVI tasks that these use cases can be categorized into?\"_, by investigating with which DL approach the VI use cases can be solved. For this, we determined four different AVI tasks to categorize the approaches. Each of these tasks aims to answer one or more questions about the inspected object. Binary classification tries to answer the question, Is the inspected object in the desired state? This applies mainly to accept/reject tasks, like separating correctly produced parts from scrap parts, regardless of the type of deficiency. Multi-class classification goes one step further, trying to answer the question, In which state is the inspected object? By additionally identifying the type of deficiency, it is possible to, e.g., distinguish between parts that are irreparably damaged and parts that can still be reworked to pass the requirements or determine the rework steps that are necessary. Localization further answers the question, Where do we find this state on the inspected object? This adds information about the locality of a state of interest, as well as enabling the finding of more than one target. It can be utilized, e.g., to check assemblies for their completeness. The fourth AVI task, multi-class localization, answers the question, Where do we find which state on the inspected object? For example, the state of a bolt can be present, missing, rusty, or cracked. Thus, the set of states is not fixed and depends, among other things, on application-specific conditions, as well as on the object under inspection. These four AVI tasks are closely related to the three most common CV tasks, image classification, object detection, and segmentation, which are visualized in Figure 5.  
In image classification, the goal is to assign a corresponding label to an image. Object detection is performed by a method or model that searches for objects of interest. Usually, the object is indicated by a rectangular bounding box, and simultaneously, object classification is performed for each object. Unlike pure classification, multiple objects can be detected and classified. Image segmentation is the process of separating every recognizable object into corresponding pixel segments. This means that both classification AVI tasks are performed by image classification models, while both localization tasks are performed by either an object detection model or a segmentation model. Figure 4: Distribution of reviewed publications by VI use cases. Figure 6 shows the composition of our publication corpus with regard to the application context, industry sector, and AVI task. The number of papers in the maintenance context outweigh those addressing manufacturing by two to one, as depicted by the outer pie chart in the center. Each of those contexts is associated with several different industrial sectors in which AVI is applied. The share of the industry sectors in each context is plotted on the left- and right-hand sides. The biggest shares in maintenance are held by the fields of civil engineering, railway, energy, and aircraft. These sum up to a total of 87.89% of all maintenance publications. The manufacturing sectors, (micro-) electronics, wrought material, agriculture, automotive, and textiles, add up to a total of 79.46% of all manufacturing papers. In addition to the industry sectors, we also group the applications per context by the AVI task. The distribution of VI tasks for each industry context is visualized by the inner pie chart. For maintenance applications, 77.01% of their total 68.3% is covered by basic and multi-class localization tasks. Only 15.7% of the tasks can be attributed to classification tasks. In manufacturing, the VI tasks are spread  
Figure 5: Visualization of the three different CV tasks—classification, object detection with two bounding boxes, and segmentation.