Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sun, Jee-Young | - |
dc.contributor.author | Jung, Seung-Won | - |
dc.contributor.author | Ko, Sung-Jea | - |
dc.date.accessioned | 2021-08-31T16:04:03Z | - |
dc.date.available | 2021-08-31T16:04:03Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58978 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | NETWORKS | - |
dc.subject | VIDEO | - |
dc.title | Lightweight Prediction and Boundary Attention-Based Semantic Segmentation for Road Scene Understanding | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Seung-Won | - |
dc.contributor.affiliatedAuthor | Ko, Sung-Jea | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3001679 | - |
dc.identifier.scopusid | 2-s2.0-85086992978 | - |
dc.identifier.wosid | 000544045000006 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.108449 - 108460 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 108449 | - |
dc.citation.endPage | 108460 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | VIDEO | - |
dc.subject.keywordAuthor | Attention mechanism | - |
dc.subject.keywordAuthor | boundary attention | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | feature refinement | - |
dc.subject.keywordAuthor | real-time semantic segmentation | - |
dc.subject.keywordAuthor | residual learning | - |
dc.subject.keywordAuthor | road scene understanding | - |
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