## 2 Background
### Metrics for Evaluation  
Several metrics exist to evaluate the performance of object detection and segmentation tasks. These are useful to assess the behavior of a new model and to compare it to existing ones. Furthermore, there are competitions, such as the Common Objects in Context Object Detection Challenge (COCO-ODC) or the PASCAL Visual Object Classes (VOC) Challenge [46], that use a combination of metrics to serve as benchmarks of quality for object detection models. Before explaining said metrics, some concepts need to be defined. Firstly, the Intersection over Union (IoU) or Jaccard Index is defined as the intersection between the predicted bounding box and the ground truth bounding box over the union of those two, or simply put, the similarity between the prediction and the ground truth. This score ranges from 0 to 1; the higher the score, the closer the prediction is to the ground truth. This metric can generate four outcomes: (1) True Positive (TP) the model detects an object where, indeed, an object exists, (2) False Positive (FP) the model detects an object where there is no object, (3) False Negative (FN) the model detects no object where there is an object present and (4) True Negative (TN) the model detects no object where there is no object. The next essential concepts are precision and recall. Precision refers to the probability of the prediction matching the ground truth. It can be defined as \(\frac{TP}{TP+FP}\) or \(\frac{P}{ALL\ DETECTED\ NOTES}\)[47; 48]. In counterpart, recall refers to TP's rate or the probability of a TP to be detected. The recall can be defined as \(\frac{TP}{TP+FN}\) or \(\frac{P}{ALL\ GROUND\frac{TP}{TRUTH\ BOXES}}\). Adding on, the precision-recall curve is a metric that summarizes the two previous concepts and is especially useful to assess the performance of models with imbalanced datasets [30]. In this curve, precision is plotted on the -axis and recall on the \(x\)-axis. Ideally, a model should deliver both high precision and recall, although this is not always the case. When comparing models, generally, the one with the highest curve indicates the best performance. Another way to gauge these two metrics is the F-measure or F1-score, defined as \(F1=2\times\frac{precision\times recall}{precision+recall}\).  
Furthermore, it is also possible to calculate average precision (AP), which, as the name indicates, is the average precision among the precision-recall curve. Mean average precision (mAP), on the other hand, involves the AP among several classes, whereas the AP only covers one. The mAP is defined as \(mAP=\frac{\sum_{i=1}^{K}AP_{i}}{K}\). Regarding the competitions mentioned above, the COCO-ODC uses 12 metrics, where mAP is the main one. On the other hand, PASCAL VOC uses two metrics: the precision-recall curve and AP. It is also worth mentioning that different challenges use different datasets with different scenarios in mind. For example, the KITTI dataset consists of hundreds of hours of driving systems gathered from an on-board camera on a car, which is especially useful when working with AVs.  
On the other hand, the COCO-ODC dataset contains many frequently found objects (80 different classes) such as animals, tools, people, etc. The PASCAL VOC data set is very similar, and both are commonly used when training object detection architectures. However, there are some key differences worth noting. For instance, annotations in COCO are facilitated by employing a JSON file, while PASCAL uses the XML format. Furthermore, the bounding box format is different: COCO uses \(xmin\) top-left, \(ymin\) top-left, width, height, while PASCAL uses \(xmin\) top-left, \(ymin\) top-left, \(xmax\) bottom-right, \(ymax\) bottom-right [46]. Although these differences may sound minimal, some researchers decided to focus on specific challenges based on the formats that they are more familiar with. Thus, there are no common grounds to compare every single algorithm ever made.