论文标题
在对象检测神经网络中进行分布外检测的运行时监视
Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks
论文作者
论文摘要
在行业中使用的真实神经网络中，运行时监视为验证提供了更现实和适用的替代方案。它对于检测分布外（OOD）的输入特别有用，该输入未经训练，可以产生错误的结果。我们将先前针对分类网络的运行时监测方法扩展到能够识别和本地化多个对象的感知系统。此外，我们在不同种类的OOD设置上通过实验分析了它的足够性，以记录我们方法的整体功效。
Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a runtime-monitoring approach previously proposed for classification networks to perception systems capable of identification and localization of multiple objects. Furthermore, we analyze its adequacy experimentally on different kinds of OOD settings, documenting the overall efficacy of our approach.
