  Visual context is one of the important clue for object detection and the
context information for boundaries of an object is especially valuable. We
propose a boundary aware network (BAN) designed to exploit the visual contexts
including boundary information and surroundings, named boundary context, and
define three types of the boundary contexts: side, vertex and in/out-boundary
context. Our BAN consists of 10 sub-networks for the area belonging to the
boundary contexts. The detection head of BAN is defined as an ensemble of these
sub-networks with different contributions depending on the sub-problem of
detection. To verify our method, we visualize the activation of the
sub-networks according to the boundary contexts and empirically show that the
sub-networks contribute more to the related sub-problem in detection. We
evaluate our method on PASCAL VOC detection benchmark and MS COCO dataset. The
proposed method achieves the mean Average Precision (mAP) of 83.4% on PASCAL
VOC and 36.9% on MS COCO. BAN allows the convolution network to provide an
additional source of contexts for detection and selectively focus on the more
important contexts, and it can be generally applied to many other detection
methods as well to enhance the accuracy in detection.
