## 4 Discussion  
Our first experiment, shown in Section 3.1, highlights the benefits of using computer generated data for training a detector on construction related scenarios when only little real world data is available. Making use of such data, we boosted our detector's precision from 86.5 % in mAP using only a small real world dataset to 94.0 % by augmenting the dataset with synthetic data. This amounts to an increase of 7.5 percentage points. Nevertheless, the experiment also shows that synthetic data samples have to be chosen carefully in order to properly increase the detection quality. Adding more data samples of similar kind to a small set of training data generally improves the detection quality. This becomes apparent when extending our dataset by the synthetic scenes 1, 2, and 3, which increases the mAP by 1.7 percentage points (see Figure 4). The detection results in Figure 6a,b illustrate that the detected regions become more accurate, which results in a higher mAP score. However, the need for more samples of one specific kind is satiated at a certain amount. The model learned by the detector, then, already considers all relevant features provided by such samples. No further insights can be drawn from such samples. As a consequence, the detection quality languishes as it occurred when adding the scenes 4, 5, and 6 to the training dataset. A further addition of more samples could cause the model to overfit to these training samples, resulting in an even worse performance on the evaluation set. The dataset extension by scene 7 points out that new samples possessing various conditions can further improve the detection results even though these are of synthetic nature. As shown by the comparison of Figure 6c,d, this enables the detector to even precisely identify workers that were only coarsely detected before due to unusual lighting conditions. Furthermore, the large amount of different training samples aggregated from the synthetic scenes 1-7 enhances the detector's generalization ability so that it is able to cope with slight occlusions and partial color changes due to overexposed lighting conditions (see Figure 6e,f). When adding too many samples of a kind, once more the results begin to languish illustrated by extending the dataset by scene 8. These findings show that computer generated data is exceedingly advantageous to augment a dataset by environmental and lighting conditions that do not occur often. Recording training data on construction sites over a long period in order to cover all conditions is a tedious task. Instead, recording only a short real world dataset at certain conditions would be sufficient while particular weather and lighting conditions could be simulated using a 3D environment. Considering the severe downscaling of the input images, the achieved mAP is already a prominent performance. Due to the scaling factor of about 0.317, the ground truth regions shrink from the original 40-50 px to a size of only 13-16 px in the scaled images. Despite the fact that CNNs generally struggle with the detection of very small objects, a precise detection by means of IoU is difficult. Each pixel offset between the detection and the ground truth label in the scaled image implies an offset of about 3 px in the original image. Accordingly, if the detection is only 1 px off, the best possible IoU decreases to 0.94. An offset of 1 px in both x- and y-direction further decreases the IoU to 0.88 at best. Taking into account that manually labeling ground truth is never accurate to a single pixel, minor deviations between detection and ground truth are inevitable. Thus, an IoU of more than 0.85 can already be assumed to be optimal in this case.