Deployed into an open world, object detectors are prone to open-set errors, false positive detections of object classes not present in the training dataset. We propose GMM-Det, a real-time method for extracting epistemic uncertainty from object detectors to identify and reject open-set errors. GMM-Det trains the detector to produce a structured logit space that is modelled with class-specific Gaussian Mixture Models. At test time, open-set errors are identified by their low log-probability under all Gaussian Mixture Models. We test two common detector architectures, Faster R-CNN and RetinaNet, across three varied datasets spanning robotics and computer vision. Our results show that GMM-Det consistently outperforms existing uncertainty techniques for identifying and rejecting open-set detections, especially at the low-error-rate operating point required for safety-critical applications. GMM-Det maintains object detection performance, and introduces only minimal computational overhead. We also introduce a methodology for converting existing object detection datasets into specific open-set datasets to evaluate open-set performance in object detection.

翻译：在开放的世界中,物体探测器容易出现开放错误,对培训数据集中不存在的物体类别进行虚假的肯定检测。我们建议GMM-Det,这是从物体探测器中提取显性不确定性的实时方法,用以识别和拒绝开立错误。GMM-Det对探测器进行结构化的登录空间培训,以生成一个与特定类别Gaussian Mixture模型相仿的结构化的登录空间。在测试时,根据所有高萨混合模型中低日志概率,发现公开错误。我们测试了两个共同的探测器结构,即快速R-CNN和Retinnet,横跨三个跨越机器人和计算机视野的不同数据集。我们的结果显示,GMM-Det一贯地超越现有识别和拒绝开立检测的不确定性技术,特别是在低eror-reser-reser-rate 操作点,以安全关键应用。GM-Dett保持了物体探测性能,并只引入了最低的计算间接数据。我们还引入了一种将现有物体探测数据集转换成特定开立数据检测功能的方法。