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.
翻译:目前,对自动驾驶或无人驾驶飞行器等安全关键应用的视觉感知堆经常使用革命神经网络(CNNs),由于这些使用案例中的安全要求,必须了解CNN的局限性,从而发现流散样品。在这项工作中,我们提出了一个方法,通过使用边际诱变(ME)损失,使OOD探测2D物体探测OOOD。提议的方法易于实施,并可用于大多数现有的物体探测结构。此外,我们引入了分离性作为在物体探测中探测OOOD样品的衡量标准。我们显示,用ME培训的有线电视新闻网损失大大超过使用标准信任分数探测OOOD的功能。与此同时,基本物体探测框架的运行时间仍然持续,使ME损失成为能够探测OD的有力工具。