## 1 Introduction
### Methodical Background  
The categorization of Machine Learning algorithms strongly depends on the learning category. According to [18; 19; 20], a distinction is typically made between supervised, unsupervised, and reinforcement learning. The classification of objects falls under supervised learning since a labeled dataset must be available. In contrast to regression, where discrete numerical values are determined, classification predicts previously defined categories, as described in [21; 22]. In [23; 24], the classification task is further divided into the number of labels per input value. If there are only two possible classes, it is called a binary classifier. A multiclass application occurs when there are several possible classes per input, but only one result label. When multiple classes are possible for one input value, this is termed a multilabel classifier. Figure 3 shows the mentioned distinction graphically. The plausibility detection presented earlier falls under the category of binary classifiers. To classify multiple new error classes, different approaches are feasible. A multilabel classification is performed by converting the classification into a regression task. Each class is converted into numerical values between 0 and 1, resulting in a vector instead of a label for each sample. Subsequently, the vector is determined via the regression algorithm. Afterwards, a threshold value (e.g., 0.5) is used to determine whether the input can be assigned to a class or not. The adaptation of multiclass classification is also an option; however, in this case, the dataset must be supplemented with additional labels. This is necessary to ensure that all label combinations are included in the dataset. With this adjustment of the dataset, multilabel classification is a viable option afterwards. Binary classifiers can also be modified for the task of multilabel classification. In this case, each binary classifier holds one label; thus, multiple models must be trained, which are evaluated afterwards. A special category in the field of Machine Learning is Deep Learning, which is often linked to the application of Neural Networks [20; 25; 26; 27]. It differs from classical Machine Learning in the number of layers and feature generation. In a classic ML model, the developer specifies the model features. In contrast, a Deep Learning model creates them in the learning phase. In [20], this process is described as automatically breaking down complicated concepts into more simple ones. An application of the Deep Learning principle to images is Convolutional Neural Networks (CNNs), which take human vision as a template. The term \"convolutional\" was first introduced by [28] in 1989 and has been associated with this type of network since then. The structure of a CNN aims to emulate the visual cortex. For this purpose, different neuron, convolutional, and pooling layers are used to filter information from the image data. One of the most famous CNNs is the LeNet of [29], which was published in 1998 to recognize handwritten letters. As computational power increased and larger datasets became  
Figure 3: Comparison of different classification approaches according to [23; 24] with positive classification results marked in green. publicly available (e.g., MNIST [29], CIFAR [30], PASCAL VOC [31], or ImageNet [32]), it was possible to put CNNs to practical and beneficial use. As a result of an image detection competition in 2012, AlexNet from [33] attracted particular attention due to its results. In comparison to LeNet-5, AlexNet has more layers and is suitable for higher resolution images. An even deeper network with 16 layers, accordingly named Vgg16, was presented by [34] two years later. All previously mentioned CNN models are serial, which allows the network to pass through all layers in sequence. According to [35], deeper networks should adapt better to a given task as the parameter space for adjustment increases, though the achieved accuracy tends to saturate and then decrease.