  In object detection, non-maximum suppression (NMS) methods are extensively
adopted to remove horizontal duplicates of detected dense boxes for generating
final object instances. However, due to the degraded quality of dense detection
boxes and not explicit exploration of the context information, existing NMS
methods via simple intersection-over-union (IoU) metrics tend to underperform
on multi-oriented and long-size objects detection. Distinguishing with general
NMS methods via duplicate removal, we propose a novel graph fusion network,
named GFNet, for multi-oriented object detection. Our GFNet is extensible and
adaptively fuse dense detection boxes to detect more accurate and holistic
multi-oriented object instances. Specifically, we first adopt a locality-aware
clustering algorithm to group dense detection boxes into different clusters. We
will construct an instance sub-graph for the detection boxes belonging to one
cluster. Then, we propose a graph-based fusion network via Graph Convolutional
Network (GCN) to learn to reason and fuse the detection boxes for generating
final instance boxes. Extensive experiments both on public available
multi-oriented text datasets (including MSRA-TD500, ICDAR2015, ICDAR2017-MLT)
and multi-oriented object datasets (DOTA) verify the effectiveness and
robustness of our method against general NMS methods in multi-oriented object
detection.
