  Convolutional Neural Networks (CNNs) are nowadays often employed in
vision-based perception stacks for safetycritical applications such as
autonomous driving or Unmanned Aerial Vehicles (UAVs). Due to the safety
requirements in those use cases, it is important to know the limitations of the
CNN and, thus, to detect Out-of-Distribution (OOD) samples. In this work, we
present an approach to enable OOD detection for 2D object detection by
employing the margin entropy (ME) loss. The proposed method is easy to
implement and can be applied to most existing object detection architectures.
In addition, we introduce Separability as a metric for detecting OOD samples in
object detection. We show that a CNN trained with the ME loss significantly
outperforms OOD detection using standard confidence scores. At the same time,
the runtime of the underlying object detection framework remains constant
rendering the ME loss a powerful tool to enable OOD detection.
