  Deployed into an open world, object detectors are prone to a type of false
positive detection termed open-set errors. 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 consistently evaluate open-set performance in object
detection. Code for GMM-Det and the dataset methodology will be made publicly
available.
