  Recent one-stage object detectors follow a per-pixel prediction approach that
predicts both the object category scores and boundary positions from every
single grid location. However, the most suitable positions for inferring
different targets, i.e., the object category and boundaries, are generally
different. Predicting all these targets from the same grid location thus may
lead to sub-optimal results. In this paper, we analyze the suitable inference
positions for object category and boundaries, and propose a
prediction-target-decoupled detector named PDNet to establish a more flexible
detection paradigm. Our PDNet with the prediction decoupling mechanism encodes
different targets separately in different locations. A learnable prediction
collection module is devised with two sets of dynamic points, i.e., dynamic
boundary points and semantic points, to collect and aggregate the predictions
from the favorable regions for localization and classification. We adopt a
two-step strategy to learn these dynamic point positions, where the prior
positions are estimated for different targets first, and the network further
predicts residual offsets to the positions with better perceptions of the
object properties. Extensive experiments on the MS COCO benchmark demonstrate
the effectiveness and efficiency of our method. With a single ResNeXt-64x4d-101
as the backbone, our detector achieves 48.7 AP with single-scale testing, which
outperforms the state-of-the-art methods by an appreciable margin under the
same experimental settings. Moreover, our detector is highly efficient as a
one-stage framework. Our code will be public.
