Abstract. 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 translationinvariance 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 6402
. Empirical studies have also shown that the
proposed method is class-agnostic to be generalised for general object
proposal