  We present Border-SegGCN, a novel architecture to improve semantic
segmentation by refining the border outline using graph convolutional networks
(GCN). The semantic segmentation network such as Unet or DeepLabV3+ is used as
a base network to have pre-segmented output. This output is converted into a
graphical structure and fed into the GCN to improve the border pixel prediction
of the pre-segmented output. We explored and studied the factors such as border
thickness, number of edges for a node, and the number of features to be fed
into the GCN by performing experiments. We demonstrate the effectiveness of the
Border-SegGCN on the CamVid and Carla dataset, achieving a test set performance
of 81.96% without any post-processing on CamVid dataset. It is higher than the
reported state of the art mIoU achieved on CamVid dataset by 0.404%
