Out-of-distribution (OoD) detection techniques for deep neural networks
(DNNs) become crucial thanks to their filtering of abnormal inputs, especially
when DNNs are used in safety-critical applications and interact with an open
and dynamic environment. Nevertheless, integrating OoD detection into
state-of-the-art (SOTA) object detection DNNs poses significant challenges,
partly due to the complexity introduced by the SOTA OoD construction methods,
which require the modification of DNN architecture and the introduction of
complex loss functions. This paper proposes a simple, yet surprisingly
effective, method that requires neither retraining nor architectural change in
object detection DNN, called Box Abstraction-based Monitors (BAM). The novelty
of BAM stems from using a finite union of convex box abstractions to capture
the learned features of objects for in-distribution (ID) data, and an important
observation that features from OoD data are more likely to fall outside of
these boxes. The union of convex regions within the feature space allows the
formation of non-convex and interpretable decision boundaries, overcoming the
limitations of VOS-like detectors without sacrificing real-time performance.
Experiments integrating BAM into Faster R-CNN-based object detection DNNs
demonstrate a considerably improved performance against SOTA OoD detection
techniques.

该论文提出了一种简单而出乎意料地有效的方法，称为基于盒子抽象的监视器（BAM），用于将离群数据检测集成到物体检测深度神经网络中，通过使用一组凸盒子对学习到的对象特征进行捕捉，并利用凸盒子之外的特征确定离群数据。将 BAM 集成到基于 Faster R-CNN 的物体检测深度神经网络中，实验证明其在离群数据检测方面相比最先进的方法有显著的性能提升。