## 4 Analysis and Discussion
### Datasets and Learning Paradigms  
The dataset dimensions vary from 45 images [48] to more than 100,000 images [81; 94; 183; 231], with the average number of images at 14,614 and a median of 1952. Figure 7 visualizes the number of datasets grouped by the decimal power of their dimension, ranging from less than 100 to less than 1,000,000. The two largest groups cover the intervals [101; 1000] and [1001; 10,000], with most of the datasets tending toward the lower boundary of their respective group. These two groups also feature the most open-access datasets: in total, 77/243 (31.69%) of the papers used at least one open-access dataset. Another important factor in relation to datasets is the uniformity of the distribution of their samples across the classes, often formulated more negatively as class imbalance. Class imbalance should be considered in learning processes since it can result in incorrect classification, detection, or segmentation. Therefore, imbalance in datasets has to be quantified differently across different learning tasks. For classification, each sample is directly associated with only one class. Object detection may feature more than one class per image, several instances of the same class, or no annotated class at all. So, to quantify the imbalance of an object detection dataset, the number of annotated objects, as well as the number of images featuring no objects, is required. Dataset imbalance for segmentation tasks can be quantified in the same way as for object detection, but the most exact measure would be to evaluate the pixel areas covered by each class in relation to the overall pixel area of the dataset, which is only reported by [PERSON] et al. and [PERSON] et al. [58; 121]. Given the number of classes n, the total number of samples, and the number of samples per class, we propose to quantify the balance in a dataset by Equation (1). The distance between the hypothetical balanced class ratio and the actual ratio between samples of a particular class and the total number of samples is used to quantify the imbalance for this class. The Euclidean distance is chosen as the distance metric for numerical stability reasons. Summing up these distances for each class yields zero for a perfectly balanced dataset and one for a completely imbalanced dataset, so subtracting it from one achieves a metric measuring the balance. Figure 7: Distribution of dataset dimensions (number of samples) utilized by publications in our publication corpus. \[\text{Balance}=1-\sum_{c=1}^{n}\sqrt{\left(\frac{samples_{c}}{samples_{total}}- \frac{1}{n}\right)^{2}} \tag{1}\]  
In total, our publication corpus encompasses 244 results produced on 204 unique datasets. For 47 publications, there is no detailed information on the training data, while 54 do not specify their test data, and in 44 cases, neither training nor testing data are sufficiently described. This means that, despite many authors providing open access to their data bases, very few researchers in the same field make use of them. This may be either due to available datasets being deemed insufficient in size, label precision, image quality, or other reasons or because they are not advertised enough and therefore not recognized by peer researchers. Overall, this makes the results less comparable and harder to reproduce and hinders further development of existing research results by others, which is detrimental to the progress of AVI. Figure 8 visualizes the relationship between model performance measured by the F1-score and the balance score defined in the last section. We chose to only include publications in this plot that report the F1-score because it is the only reported metric that deals well with balanced as well as imbalanced datasets. The chart on the left shows a mostly linear correlation between balance and performance in classification tasks, with a few upward and downward outliers. These are predominantly from the multi-class versions. Binary classification has a lot more publications based on entirely balanced datasets.