  Due to the simpleness and high efficiency, single-stage object detectors have
been widely applied in many computer vision applications . However, the low
correlation between the classification score and localization accuracy of the
predicted detections has severely hurt the localization accuracy of models. In
this paper, IoU-aware single-stage object detector is proposed to solve this
problem. Specifically, IoU-aware single-stage object detector predicts the IoU
for each detected box. Then the classification score and predicted IoU are
multiplied to compute the final detection confidence, which is more correlated
with the localization accuracy. The detection confidence is then used as the
input of the subsequent NMS and COCO AP computation, which will substantially
improve the localization accuracy of models. Sufficient experiments on COCO and
PASCAL VOC datasets demonstrate the effectiveness of IoU-aware single-stage
object detector on improving model's localization accuracy. Without whistles
and bells, the proposed method can substantially improve AP by $1.7\%\sim1.9\%$
and AP75 by $2.2\%\sim2.5\%$ on COCO \textit{test-dev}. On PASCAL VOC, the
proposed method can substantially improve AP by $2.9\%\sim4.4\%$ and AP80, AP90
by $4.6\%\sim10.2\%$. Code is available here:
{https://github.com/ShengkaiWu/IoU-aware-single-stage-object-detector}.
