Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for
  Accurate Object Detection
Yilong Lv Min Li Yujie He Shaopeng Li Zhuzhen He Aitao Yang
Anchor-based detectors have been continuously developed for object detection.
However, the individual anchor box makes it difficult to predict the boundary's
offset accurately. Instead of taking each bounding box as a closed individual,
we consider using multiple boxes together to get prediction boxes. To this end,
this paper proposes the \textbf{Box Decouple-Couple(BDC) strategy} in the
inference, which no longer discards the overlapping boxes, but decouples the
corner points of these boxes. Then, according to each corner's score, we couple
the corner points to select the most accurate corner pairs. To meet the BDC
strategy, a simple but novel model is designed named the
\textbf{Anchor-Intermediate Detector(AID)}, which contains two head networks,
i.e., an anchor-based head and an anchor-free \textbf{Corner-aware head}. The
corner-aware head is able to score the corners of each bounding box to
facilitate the coupling between corner points. Extensive experiments on MS COCO
show that the proposed anchor-intermediate detector respectively outperforms
their baseline RetinaNet and GFL method by $\sim$2.4 and $\sim$1.2 AP on the MS
COCO test-dev dataset without any bells and whistles. Code is available at:
https://github.com/YilongLv/AID.