  Most existing object detectors suffer from class imbalance problems that
hinder balanced performance. In particular, anchor free object detectors have
to solve the background imbalance problem due to detection in a per-pixel
prediction fashion as well as foreground imbalance problem simultaneously. In
this work, we propose Balance-oriented focal loss that can induce balanced
learning by considering both background and foreground balance comprehensively.
This work aims to address imbalance problem in the situation of using a general
unbalanced data of non-extreme distribution not including few shot and the
focal loss for anchor free object detector. We use a batch-wise alpha-balanced
variant of the focal loss to deal with this imbalance problem elaborately. It
is a simple and practical solution using only re-weighting for general
unbalanced data. It does require neither additional learning cost nor
structural change during inference and grouping classes is also unnecessary.
Through extensive experiments, we show the performance improvement for each
component and analyze the effect of linear scheduling when using re-weighting
for the loss. By improving the focal loss in terms of balancing foreground
classes, our method achieves AP gains of +1.2 in MS-COCO for the anchor free
real-time detector.
