Object detection is an important tool in many areas, such as robotics or autonomous driving. Especially in these areas, a wide variety of object classes must be detected or interacted with. Models from the field of Open-Vocabulary Detection (OVD) provide a solution here, as they can detect not only base classes but also novel object classes, i.e., those classes that were not seen during training. However, one problem with OVD models is their poor calibration, meaning that the predictions are often too over- or under-confident. To improve the calibration, Temperature Scaling is used in this study. Using YOLO World, one of the best-performing OVD models, the aim is to determine the optimal T-value for this calibration method. For this reason, it is investigated whether there is a correlation between the logit distribution and the optimal T-value and how this can be modeled. Finally, the influence of Temperature Scaling on the Expected Calibration Error (ECE) and the mAP (Mean Average Precision) will be analyzed. The results of this study show that similar logit distributions of different datasets result in the same optimal T-values. This correlation could be best modeled using Kernel Ridge Regression (KRR) and Support Vector Machine (SVM). In all cases, the ECE could be improved by Temperature Scaling without significantly reducing the mAP.