# Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network

Source: [Border-SegGCN: Improving Semantic Segmentation by Refining the Border Outline using Graph Convolutional Network](<https://www.sota2.com/research/paper/68fa6ab41e209131cea29e36>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:31:18.149Z

Authors: Naina Dhingra, George Chogovadze, Andreas Kunz

Published: 2021-09-11T19:34:04.000Z

## Abstract

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%

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.05353>)

[PDF](<https://arxiv.org/pdf/2109.05353.pdf>)