  Accurately localising object proposals is an important precondition for high
detection rate for the state-of-the-art object detection frameworks. The
accuracy of an object detection method has been shown highly related to the
average recall (AR) of the proposals. In this work, we propose an advanced
object proposal network in favour of translation-invariance for objectness
classification, translation-variance for bounding box regression, large
effective receptive fields for capturing global context and scale-invariance
for dealing with a range of object sizes from extremely small to large. The
design of the network architecture aims to be simple while being effective and
with real time performance. Without bells and whistles the proposed object
proposal network significantly improves the AR at 1,000 proposals by $35\%$ and
$45\%$ on PASCAL VOC and COCO dataset respectively and has a fast inference
time of 44.8 ms for input image size of $640^{2}$. Empirical studies have also
shown that the proposed method is class-agnostic to be generalised for general
object proposal.
