  Since many safety-critical systems, such as surgical robots and autonomous
driving cars operate in unstable environments with sensor noise and incomplete
data, it is desirable for object detectors to take the localization uncertainty
into account. However, there are several limitations of the existing
uncertainty estimation methods for anchor-based object detection. 1) They model
the uncertainty of the heterogeneous object properties with different
characteristics and scales, such as location (center point) and scale (width,
height), which could be difficult to estimate. 2) They model box offsets as
Gaussian distributions, which is not compatible with the ground truth bounding
boxes that follow the Dirac delta distribution. 3) Since anchor-based methods
are sensitive to anchor hyper-parameters, their localization uncertainty could
also be highly sensitive to the choice of hyper-parameters. To tackle these
limitations, we propose a new localization uncertainty estimation method called
UAD for anchor-free object detection. Our method captures the uncertainty in
four directions of box offsets (left, right, top, bottom) that are homogeneous,
so that it can tell which direction is uncertain, and provide a quantitative
value of uncertainty in [0, 1]. To enable such uncertainty estimation, we
design a new uncertainty loss, negative power log-likelihood loss, to measure
the localization uncertainty by weighting the likelihood loss by its IoU, which
alleviates the model misspecification problem. Furthermore, we propose an
uncertainty-aware focal loss for reflecting the estimated uncertainty to the
classification score. Experimental results on COCO datasets demonstrate that
our method significantly improves FCOS, by up to 1.8 points, without
sacrificing computational efficiency.
