  Calibrated confidence estimates obtained from neural networks are crucial,
particularly for safety-critical applications such as autonomous driving or
medical image diagnosis. However, although the task of confidence calibration
has been investigated on classification problems, thorough investigations on
object detection and segmentation problems are still missing. Therefore, we
focus on the investigation of confidence calibration for object detection and
segmentation models in this chapter. We introduce the concept of multivariate
confidence calibration that is an extension of well-known calibration methods
to the task of object detection and segmentation. This allows for an extended
confidence calibration that is also aware of additional features such as
bounding box/pixel position, shape information, etc. Furthermore, we extend the
expected calibration error (ECE) to measure miscalibration of object detection
and segmentation models. We examine several network architectures on MS COCO as
well as on Cityscapes and show that especially object detection as well as
instance segmentation models are intrinsically miscalibrated given the
introduced definition of calibration. Using our proposed calibration methods,
we have been able to improve calibration so that it also has a positive impact
on the quality of segmentation masks as well.
