  In object detection, bounding box regression (BBR) is a crucial step that
determines the object localization performance. However, we find that most
previous loss functions for BBR have two main drawbacks: (i) Both $\ell_n$-norm
and IOU-based loss functions are inefficient to depict the objective of BBR,
which leads to slow convergence and inaccurate regression results. (ii) Most of
the loss functions ignore the imbalance problem in BBR that the large number of
anchor boxes which have small overlaps with the target boxes contribute most to
the optimization of BBR. To mitigate the adverse effects caused thereby, we
perform thorough studies to exploit the potential of BBR losses in this paper.
Firstly, an Efficient Intersection over Union (EIOU) loss is proposed, which
explicitly measures the discrepancies of three geometric factors in BBR, i.e.,
the overlap area, the central point and the side length. After that, we state
the Effective Example Mining (EEM) problem and propose a regression version of
focal loss to make the regression process focus on high-quality anchor boxes.
Finally, the above two parts are combined to obtain a new loss function, namely
Focal-EIOU loss. Extensive experiments on both synthetic and real datasets are
performed. Notable superiorities on both the convergence speed and the
localization accuracy can be achieved over other BBR losses.
