  Confidence calibration is a major concern when applying artificial neural
networks in safety-critical applications. Since most research in this area has
focused on classification in the past, confidence calibration in the scope of
object detection has gained more attention only recently. Based on previous
work, we study the miscalibration of object detection models with respect to
image location and box scale. Our main contribution is to additionally consider
the impact of box selection methods like non-maximum suppression to
calibration. We investigate the default intrinsic calibration of object
detection models and how it is affected by these post-processing techniques.
For this purpose, we distinguish between black-box calibration with non-maximum
suppression and white-box calibration with raw network outputs. Our experiments
reveal that post-processing highly affects confidence calibration. We show that
non-maximum suppression has the potential to degrade initially well-calibrated
predictions, leading to overconfident and thus miscalibrated models.
