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.

本篇论文探讨使用卷积神经网络（CNN）的视觉感知技术中，如何利用平衡熵（ME）误差来实现 2D 物体检测中的 Out-of-Distribution 检测。 该方法易于实现，并可应用于大多数现有的物体检测架构，同时介绍了分离度作为检测 OOD 样本的度量标准。 同时，本文发现用 ME 损失训练的 CNN 在 OOD 检测方面明显优于使用标准置信分数的检测，并且底层物体检测框架的运行时间仍保持不变，从而使 ME 损失成为启用 OOD 检测的强大工具。