Identifying Out-of-Distribution Samples in Real-Time for Safety-Critical 2D Object Detection with Margin Entropy Loss. (arXiv:2209.00364v1 [cs.CV])

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 …

arxiv detection distribution entropy loss real-time safety safety-critical time