A Comparison Study of Neural Network-Based Semantic Segmentation for Off-Road Traversability Article Swipe
Semih Beyçimen
,
Dmitry Ignatyev
,
Argyrios Zolotas
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31256/wt3yp1e
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.31256/wt3yp1e
This paper presents work from a PhD study on unmanned ground vehicle advanced traversability.In particular, in this paper a number of learning algorithm have been trained and tested using the YAMAHA dataset (an off-road related dataset).Results were analysed and compared in terms of prediction accuracy and training time.It was noted that while various models provide appropriate accuracy results, only few provide results that can be classed as optimal when training time is considered.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.31256/wt3yp1e
- https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309217393
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4309217393Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.31256/wt3yp1eDigital Object Identifier
- Title
-
A Comparison Study of Neural Network-Based Semantic Segmentation for Off-Road TraversabilityWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-11Full publication date if available
- Authors
-
Semih Beyçimen, Dmitry Ignatyev, Argyrios ZolotasList of authors in order
- Landing page
-
https://doi.org/10.31256/wt3yp1ePublisher landing page
- PDF URL
-
https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdfDirect OA link when available
- Concepts
-
Computer science, Segmentation, Artificial neural network, Artificial intelligence, Training (meteorology), Machine learning, Work (physics), Ground truth, Training set, Pattern recognition (psychology), Data mining, Engineering, Geography, Mechanical engineering, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
5Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4309217393 |
|---|---|
| doi | https://doi.org/10.31256/wt3yp1e |
| ids.doi | https://doi.org/10.31256/wt3yp1e |
| ids.openalex | https://openalex.org/W4309217393 |
| fwci | 0.33818076 |
| type | article |
| title | A Comparison Study of Neural Network-Based Semantic Segmentation for Off-Road Traversability |
| biblio.issue | |
| biblio.volume | 5 |
| biblio.last_page | 73 |
| biblio.first_page | 72 |
| topics[0].id | https://openalex.org/T10191 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Robotics and Sensor-Based Localization |
| topics[1].id | https://openalex.org/T10586 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9973999857902527 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Robotic Path Planning Algorithms |
| topics[2].id | https://openalex.org/T11211 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9901999831199646 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1907 |
| topics[2].subfield.display_name | Geology |
| topics[2].display_name | 3D Surveying and Cultural Heritage |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7537760138511658 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6678156852722168 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C50644808 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6440679430961609 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[2].display_name | Artificial neural network |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6436880826950073 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C2777211547 |
| concepts[4].level | 2 |
| concepts[4].score | 0.582073986530304 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17141490 |
| concepts[4].display_name | Training (meteorology) |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5283618569374084 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C18762648 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4440394639968872 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q42213 |
| concepts[6].display_name | Work (physics) |
| concepts[7].id | https://openalex.org/C146849305 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44284167885780334 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q370766 |
| concepts[7].display_name | Ground truth |
| concepts[8].id | https://openalex.org/C51632099 |
| concepts[8].level | 2 |
| concepts[8].score | 0.41227248311042786 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3985153 |
| concepts[8].display_name | Training set |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.3823391795158386 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C124101348 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3219130337238312 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[10].display_name | Data mining |
| concepts[11].id | https://openalex.org/C127413603 |
| concepts[11].level | 0 |
| concepts[11].score | 0.10589465498924255 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[11].display_name | Engineering |
| concepts[12].id | https://openalex.org/C205649164 |
| concepts[12].level | 0 |
| concepts[12].score | 0.07778716087341309 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[12].display_name | Geography |
| concepts[13].id | https://openalex.org/C78519656 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[13].display_name | Mechanical engineering |
| concepts[14].id | https://openalex.org/C153294291 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[14].display_name | Meteorology |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7537760138511658 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.6678156852722168 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[2].score | 0.6440679430961609 |
| keywords[2].display_name | Artificial neural network |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6436880826950073 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/training |
| keywords[4].score | 0.582073986530304 |
| keywords[4].display_name | Training (meteorology) |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.5283618569374084 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/work |
| keywords[6].score | 0.4440394639968872 |
| keywords[6].display_name | Work (physics) |
| keywords[7].id | https://openalex.org/keywords/ground-truth |
| keywords[7].score | 0.44284167885780334 |
| keywords[7].display_name | Ground truth |
| keywords[8].id | https://openalex.org/keywords/training-set |
| keywords[8].score | 0.41227248311042786 |
| keywords[8].display_name | Training set |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.3823391795158386 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/data-mining |
| keywords[10].score | 0.3219130337238312 |
| keywords[10].display_name | Data mining |
| keywords[11].id | https://openalex.org/keywords/engineering |
| keywords[11].score | 0.10589465498924255 |
| keywords[11].display_name | Engineering |
| keywords[12].id | https://openalex.org/keywords/geography |
| keywords[12].score | 0.07778716087341309 |
| keywords[12].display_name | Geography |
| language | en |
| locations[0].id | doi:10.31256/wt3yp1e |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4220651422 |
| locations[0].source.issn | 2516-502X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2516-502X |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of robotics & autonomous systems |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | UK-RAS Conference for PhD and Early Career Researchers Proceedings |
| locations[0].landing_page_url | https://doi.org/10.31256/wt3yp1e |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5058603607 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0213-3718 |
| authorships[0].author.display_name | Semih Beyçimen |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[0].affiliations[0].raw_affiliation_string | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| authorships[0].institutions[0].id | https://openalex.org/I82284825 |
| authorships[0].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Cranfield University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Semih Beycimen |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| authorships[1].author.id | https://openalex.org/A5038662808 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3627-3740 |
| authorships[1].author.display_name | Dmitry Ignatyev |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[1].affiliations[0].raw_affiliation_string | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| authorships[1].institutions[0].id | https://openalex.org/I82284825 |
| authorships[1].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Cranfield University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dmitry Ignatyev |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| authorships[2].author.id | https://openalex.org/A5089252134 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2829-1298 |
| authorships[2].author.display_name | Argyrios Zolotas |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[2].affiliations[0].raw_affiliation_string | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| authorships[2].institutions[0].id | https://openalex.org/I82284825 |
| authorships[2].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | Cranfield University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Argyrios Zolotas |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Centre for Autonomous and Cyber-Physical Systems, SATM Cranfield University, Bedford, UK |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdf |
| open_access.oa_status | bronze |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Comparison Study of Neural Network-Based Semantic Segmentation for Off-Road Traversability |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10191 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Robotics and Sensor-Based Localization |
| related_works | https://openalex.org/W230091440, https://openalex.org/W2233261550, https://openalex.org/W4295532600, https://openalex.org/W2810751659, https://openalex.org/W258997015, https://openalex.org/W2063823869, https://openalex.org/W1997160662, https://openalex.org/W4394050964, https://openalex.org/W4401571341, https://openalex.org/W2551249631 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.31256/wt3yp1e |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4220651422 |
| best_oa_location.source.issn | 2516-502X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2516-502X |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of robotics & autonomous systems |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | proceedings-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | UK-RAS Conference for PhD and Early Career Researchers Proceedings |
| best_oa_location.landing_page_url | https://doi.org/10.31256/wt3yp1e |
| primary_location.id | doi:10.31256/wt3yp1e |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4220651422 |
| primary_location.source.issn | 2516-502X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2516-502X |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of robotics & autonomous systems |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://www.ukras.org.uk/wp-content/uploads/formidable/21/UK-RAS2022_proceedings_Paper-34.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | UK-RAS Conference for PhD and Early Career Researchers Proceedings |
| primary_location.landing_page_url | https://doi.org/10.31256/wt3yp1e |
| publication_date | 2022-11-11 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3099155473, https://openalex.org/W2913921102, https://openalex.org/W3120993684, https://openalex.org/W4220941930, https://openalex.org/W2765757095 |
| referenced_works_count | 5 |
| abstract_inverted_index.a | 5, 18 |
| abstract_inverted_index.as | 66 |
| abstract_inverted_index.be | 64 |
| abstract_inverted_index.in | 15, 40 |
| abstract_inverted_index.is | 71 |
| abstract_inverted_index.of | 20, 42 |
| abstract_inverted_index.on | 8 |
| abstract_inverted_index.(an | 32 |
| abstract_inverted_index.PhD | 6 |
| abstract_inverted_index.and | 26, 38, 45 |
| abstract_inverted_index.can | 63 |
| abstract_inverted_index.few | 59 |
| abstract_inverted_index.the | 29 |
| abstract_inverted_index.was | 48 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.been | 24 |
| abstract_inverted_index.from | 4 |
| abstract_inverted_index.have | 23 |
| abstract_inverted_index.only | 58 |
| abstract_inverted_index.that | 50, 62 |
| abstract_inverted_index.this | 16 |
| abstract_inverted_index.time | 70 |
| abstract_inverted_index.were | 36 |
| abstract_inverted_index.when | 68 |
| abstract_inverted_index.work | 3 |
| abstract_inverted_index.noted | 49 |
| abstract_inverted_index.paper | 1, 17 |
| abstract_inverted_index.study | 7 |
| abstract_inverted_index.terms | 41 |
| abstract_inverted_index.using | 28 |
| abstract_inverted_index.while | 51 |
| abstract_inverted_index.YAMAHA | 30 |
| abstract_inverted_index.ground | 10 |
| abstract_inverted_index.models | 53 |
| abstract_inverted_index.number | 19 |
| abstract_inverted_index.tested | 27 |
| abstract_inverted_index.classed | 65 |
| abstract_inverted_index.dataset | 31 |
| abstract_inverted_index.optimal | 67 |
| abstract_inverted_index.provide | 54, 60 |
| abstract_inverted_index.related | 34 |
| abstract_inverted_index.results | 61 |
| abstract_inverted_index.time.It | 47 |
| abstract_inverted_index.trained | 25 |
| abstract_inverted_index.various | 52 |
| abstract_inverted_index.vehicle | 11 |
| abstract_inverted_index.accuracy | 44, 56 |
| abstract_inverted_index.advanced | 12 |
| abstract_inverted_index.analysed | 37 |
| abstract_inverted_index.compared | 39 |
| abstract_inverted_index.learning | 21 |
| abstract_inverted_index.off-road | 33 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.results, | 57 |
| abstract_inverted_index.training | 46, 69 |
| abstract_inverted_index.unmanned | 9 |
| abstract_inverted_index.algorithm | 22 |
| abstract_inverted_index.prediction | 43 |
| abstract_inverted_index.appropriate | 55 |
| abstract_inverted_index.considered. | 72 |
| abstract_inverted_index.particular, | 14 |
| abstract_inverted_index.dataset).Results | 35 |
| abstract_inverted_index.traversability.In | 13 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.75107722 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |