## 4 Analysis and Discussion
### Performance Evaluation by AVI Task  
The performance of the DL models utilized is not as easily comparable because of the heterogeneity of reported metrics as well as datasets. In total, ten different metrics were reported, namely, accuracy (Acc), precision (Pre), recall (Rec), F1-score (F1), true-negative rate (TNR), true-positive rate (TNR), mean average precision (mAP), mean average precision at 50% as well as 75% intersection over union (mAP50, mAP75), and mean intersection over union (mIoU). Figure 9 shows how often a certain metric was utilized for applications that use classification, object detection, and segmentation (from top to bottom). The first thing that stands out is the fact that accuracy is still the most common metric for classification tasks, despite having low descriptive quality, especially with imbalanced datasets, which are quite common, as stated in Section 4.2. The F1-score, which is the harmonic mean of precision and recall, is the second most common metric. Precision and recall are tied for third place. Among object detection use cases, the mean average precision (mAP) is the most reported metric, probably because it is also the official metric of the MS COCO object detection challenge. The F1-score and recall are also very relevant in object detection tasks, with the second-most occurrences in the surveyed literature, while accuracy is in third place. Segmentation tasks are predominantly evaluated by their F1-score and mean intersection over union (mIoU) between predictions and the ground truth. Recall is in the top three as well for the last CV task. In the following, we will first analyze the performance of the models employed in the application use cases to derive recommendations for each AVI task based on the  
Figure 9: Distribution of metrics used in papers in our publication corpus grouped by CV task. most reported metrics. These are accuracy as well as F1-score for (binary and multi-class) classification and mAP as well as F1-score for (binary and multi-class) localization. If precision and recall were reported but F1-score was not, we calculated it based on them and take it into account as well. To be able to provide recommendations for segmentation models as well, we will also look at the mIoU, as this seems to be a metric reported almost exclusively for this CV task. After this, we will also look at the performance on the benchmark datasets from Table 6 and determine whether there are any differences. Custom CNN LSTM networks show the best median accuracy, as depicted in Figure 10, but this does not have the highest expressive value, with a sample size of four and all results coming from the same paper [74]. The same applies to MLPs and ViT, which show a higher median accuracy than all other models with two to four occurrences. Despite their good performance, no general recommendation can be given for MLPs because all of them employ hand-engineered input features. EfficientNet, on the contrary, is very close performance-wise but was applied to five datasets by four different authors. ResNet and VGG are the best two architectures, with considerable sample sizes of nine and eight, respectively. While the accuracy distribution for VGG shows less spread, meaning more consistent performance over different tasks, ResNet's median performance is almost as good. Custom CNNs have the third-worst median accuracy and a very large spread, as well. As this category contains very different architectures, this is to be expected. So, all in all, the best-performing choice for solving classification tasks based on accuracy as the metric from our publication corpus is EfficientNet. When looking at the classification F1-scores in Figure 11, we generally see similar median values and a higher spread compared to accuracy. This is unexpected but could be an indicator that most of the datasets do not show strong imbalances. As for accuracy, EfficientNet has the highest median F1-score, closely followed by ViT. MLPs have the third-highest median F1-score and the third-narrowest interquartile range.