  Deep learning object detectors often return false positives with very high
confidence. Although they optimize generic detection performance, such as mean
average precision (mAP), they are not designed for reliability. For a reliable
detection system, if a high confidence detection is made, we would want high
certainty that the object has indeed been detected. To achieve this, we have
developed a set of verification tests which a proposed detection must pass to
be accepted. We develop a theoretical framework which proves that, under
certain assumptions, our verification tests will not accept any false
positives. Based on an approximation to this framework, we present a practical
detection system that can verify, with high precision, whether each detection
of a machine-learning based object detector is correct. We show that these
tests can improve the overall accuracy of a base detector and that accepted
examples are highly likely to be correct. This allows the detector to operate
in a high precision regime and can thus be used for robotic perception systems
as a reliable instance detection method. Code is available at
https://github.com/siddancha/FlowVerify.
