  Object detectors are at the heart of many semi- and fully autonomous decision
systems and are poised to become even more indispensable. They are, however,
still lacking in accessibility and can sometimes produce unreliable
predictions. Especially concerning in this regard are the -- essentially
hand-crafted -- non-maximum suppression algorithms that lead to an obfuscated
prediction process and biased confidence estimates. We show that we can
eliminate classic NMS-style post-processing by using IoU-aware calibration.
IoU-aware calibration is a conditional Beta calibration; this makes it
parallelizable with no hyper-parameters. Instead of arbitrary cutoffs or
discounts, it implicitly accounts for the likelihood of each detection being a
duplicate and adjusts the confidence score accordingly, resulting in
empirically based precision estimates for each detection. Our extensive
experiments on diverse detection architectures show that the proposed IoU-aware
calibration can successfully model duplicate detections and improve
calibration. Compared to the standard sequential NMS and calibration approach,
our joint modeling can deliver performance gains over the best NMS-based
alternative while producing consistently better-calibrated confidence
predictions with less complexity. The
\hyperlink{https://github.com/Blueblue4/IoU-AwareCalibration}{code} for all our
experiments is publicly available.
