Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding

Jee Young Sun, Seung Won Jung, Sung Jea Ko

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Semantic segmentation is one of the most commonly used techniques for road scene understanding. Recently developed deep learning-based semantic segmentation networks are typically based on the encoder-decoder structure and have made great progress in road scene understanding. However, these conventional networks still encounter difficulties in recovering spatial details. To overcome this problem, we introduce a lightweight prediction and boundary-aware refinement module that can hierarchically refine the segmentation results with spatial details. The proposed refinement module has two attention units called the upper-level prediction attention unit and the upper-level boundary attention unit. The upper-level prediction attention unit emphasizes the features in the regions that need to be refined by using predicted class probability from the upper-level, whereas the upper-level boundary attention unit focuses on the features near the semantic boundary of the upper-level segmentation result. By using the proposed prediction and boundary-aware refinement module in the decoder network, the segmentation result can gradually be improved in a top-down manner to a finer and more complete one. Experimental results on the Cityscapes and CamVid datasets demonstrate that the proposed prediction and boundary attention-based refinement module can achieve considerable performance improvement in segmentation accuracy with a marginal increase in computational complexity.

Original languageEnglish
Article number9114881
Pages (from-to)108449-108460
Number of pages12
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Attention mechanism
  • boundary attention
  • deep learning
  • feature refinement
  • real-time semantic segmentation
  • residual learning
  • road scene understanding

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • Electrical and Electronic Engineering
  • General Materials Science


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