  Anchor free methods have defined the new frontier in state-of-the-art object
detection researches where accurate bounding box estimation is the key to the
success of these methods. However, even the bounding box has the highest
confidence score, it is still far from perfect at localization. To this end, we
propose a box reorganization method(DDBNet), which can dive deeper into the box
for more accurate localization. At the first step, drifted boxes are filtered
out because the contents in these boxes are inconsistent with target semantics.
Next, the selected boxes are broken into boundaries, and the well-aligned
boundaries are searched and grouped into a sort of optimal boxes toward
tightening instances more precisely. Experimental results show that our method
is effective which leads to state-of-the-art performance for object detection.
