Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot Article Swipe
Balakrishnan Ramalingam
,
Abdullah Aamir Hayat
,
Mohan Rajesh Elara
,
Braulio Félix Gómez
,
Lim Yi
,
Thejus Pathmakumar
,
Madan Mohan Rayguru
,
Selvasundari Subramanian
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21082595
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21082595
The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21082595
- https://www.mdpi.com/1424-8220/21/8/2595/pdf
- OA Status
- gold
- Cited By
- 48
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3144878405
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3144878405Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21082595Digital Object Identifier
- Title
-
Deep Learning Based Pavement Inspection Using Self-Reconfigurable RobotWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-07Full publication date if available
- Authors
-
Balakrishnan Ramalingam, Abdullah Aamir Hayat, Mohan Rajesh Elara, Braulio Félix Gómez, Lim Yi, Thejus Pathmakumar, Madan Mohan Rayguru, Selvasundari SubramanianList of authors in order
- Landing page
-
https://doi.org/10.3390/s21082595Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/8/2595/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/21/8/2595/pdfDirect OA link when available
- Concepts
-
Garbage, Computer science, Artificial intelligence, Convolutional neural network, Object detection, Deep learning, Segmentation, Robot, Computer vision, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
48Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 10, 2023: 6, 2022: 16, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3144878405 |
|---|---|
| doi | https://doi.org/10.3390/s21082595 |
| ids.doi | https://doi.org/10.3390/s21082595 |
| ids.mag | 3144878405 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33917223 |
| ids.openalex | https://openalex.org/W3144878405 |
| fwci | 5.32273989 |
| type | article |
| title | Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot |
| biblio.issue | 8 |
| biblio.volume | 21 |
| biblio.last_page | 2595 |
| biblio.first_page | 2595 |
| topics[0].id | https://openalex.org/T11606 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2205 |
| topics[0].subfield.display_name | Civil and Structural Engineering |
| topics[0].display_name | Infrastructure Maintenance and Monitoring |
| topics[1].id | https://openalex.org/T10264 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9797999858856201 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2205 |
| topics[1].subfield.display_name | Civil and Structural Engineering |
| topics[1].display_name | Asphalt Pavement Performance Evaluation |
| topics[2].id | https://openalex.org/T11850 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9616000056266785 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2205 |
| topics[2].subfield.display_name | Civil and Structural Engineering |
| topics[2].display_name | Concrete Corrosion and Durability |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C75403996 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7965846657752991 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5521979 |
| concepts[0].display_name | Garbage |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.672144889831543 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6187185645103455 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C81363708 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6138855218887329 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[3].display_name | Convolutional neural network |
| concepts[4].id | https://openalex.org/C2776151529 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5585590600967407 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3045304 |
| concepts[4].display_name | Object detection |
| concepts[5].id | https://openalex.org/C108583219 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5463138818740845 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[5].display_name | Deep learning |
| concepts[6].id | https://openalex.org/C89600930 |
| concepts[6].level | 2 |
| concepts[6].score | 0.544008731842041 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[6].display_name | Segmentation |
| concepts[7].id | https://openalex.org/C90509273 |
| concepts[7].level | 2 |
| concepts[7].score | 0.49552977085113525 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11012 |
| concepts[7].display_name | Robot |
| concepts[8].id | https://openalex.org/C31972630 |
| concepts[8].level | 1 |
| concepts[8].score | 0.46028369665145874 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[8].display_name | Computer vision |
| concepts[9].id | https://openalex.org/C199360897 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[9].display_name | Programming language |
| keywords[0].id | https://openalex.org/keywords/garbage |
| keywords[0].score | 0.7965846657752991 |
| keywords[0].display_name | Garbage |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.672144889831543 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6187185645103455 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[3].score | 0.6138855218887329 |
| keywords[3].display_name | Convolutional neural network |
| keywords[4].id | https://openalex.org/keywords/object-detection |
| keywords[4].score | 0.5585590600967407 |
| keywords[4].display_name | Object detection |
| keywords[5].id | https://openalex.org/keywords/deep-learning |
| keywords[5].score | 0.5463138818740845 |
| keywords[5].display_name | Deep learning |
| keywords[6].id | https://openalex.org/keywords/segmentation |
| keywords[6].score | 0.544008731842041 |
| keywords[6].display_name | Segmentation |
| keywords[7].id | https://openalex.org/keywords/robot |
| keywords[7].score | 0.49552977085113525 |
| keywords[7].display_name | Robot |
| keywords[8].id | https://openalex.org/keywords/computer-vision |
| keywords[8].score | 0.46028369665145874 |
| keywords[8].display_name | Computer vision |
| language | en |
| locations[0].id | doi:10.3390/s21082595 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1424-8220/21/8/2595/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s21082595 |
| locations[1].id | pmid:33917223 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33917223 |
| locations[2].id | pmh:oai:doaj.org/article:05c097fc689f4d3ca248a2717fe3a1c8 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 21, Iss 8, p 2595 (2021) |
| locations[2].landing_page_url | https://doaj.org/article/05c097fc689f4d3ca248a2717fe3a1c8 |
| locations[3].id | pmh:oai:mdpi.com:/1424-8220/21/8/2595/ |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400947 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | True |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | MDPI (MDPI AG) |
| locations[3].source.host_organization | https://openalex.org/I4210097602 |
| locations[3].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[3].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors; Volume 21; Issue 8; Pages: 2595 |
| locations[3].landing_page_url | https://dx.doi.org/10.3390/s21082595 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:8067980 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | other-oa |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/other-oa |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Sensors (Basel) |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/8067980 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5057929288 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3243-9814 |
| authorships[0].author.display_name | Balakrishnan Ramalingam |
| authorships[0].countries | SG |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[0].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[0].institutions[0].id | https://openalex.org/I152815399 |
| authorships[0].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[0].institutions[0].country_code | SG |
| authorships[0].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Balakrishnan Ramalingam |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[1].author.id | https://openalex.org/A5086990384 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6141-4600 |
| authorships[1].author.display_name | Abdullah Aamir Hayat |
| authorships[1].countries | SG |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[1].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[1].institutions[0].id | https://openalex.org/I152815399 |
| authorships[1].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[1].institutions[0].country_code | SG |
| authorships[1].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Abdullah Aamir Hayat |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[2].author.id | https://openalex.org/A5041057723 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6504-1530 |
| authorships[2].author.display_name | Mohan Rajesh Elara |
| authorships[2].countries | SG |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[2].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[2].institutions[0].id | https://openalex.org/I152815399 |
| authorships[2].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[2].institutions[0].country_code | SG |
| authorships[2].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohan Rajesh Elara |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[3].author.id | https://openalex.org/A5068621996 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1425-2319 |
| authorships[3].author.display_name | Braulio Félix Gómez |
| authorships[3].countries | SG |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[3].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[3].institutions[0].id | https://openalex.org/I152815399 |
| authorships[3].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[3].institutions[0].country_code | SG |
| authorships[3].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Braulio Félix Gómez |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[4].author.id | https://openalex.org/A5023875296 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2997-6667 |
| authorships[4].author.display_name | Lim Yi |
| authorships[4].countries | SG |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[4].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[4].institutions[0].id | https://openalex.org/I152815399 |
| authorships[4].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[4].institutions[0].country_code | SG |
| authorships[4].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Lim Yi |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[5].author.id | https://openalex.org/A5089170493 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-4010-570X |
| authorships[5].author.display_name | Thejus Pathmakumar |
| authorships[5].countries | SG |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[5].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[5].institutions[0].id | https://openalex.org/I152815399 |
| authorships[5].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[5].institutions[0].country_code | SG |
| authorships[5].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Thejus Pathmakumar |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[6].author.id | https://openalex.org/A5108024514 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-4426-6364 |
| authorships[6].author.display_name | Madan Mohan Rayguru |
| authorships[6].countries | SG |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I152815399 |
| authorships[6].affiliations[0].raw_affiliation_string | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[6].institutions[0].id | https://openalex.org/I152815399 |
| authorships[6].institutions[0].ror | https://ror.org/05j6fvn87 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I152815399 |
| authorships[6].institutions[0].country_code | SG |
| authorships[6].institutions[0].display_name | Singapore University of Technology and Design |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Madan Mohan Rayguru |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore |
| authorships[7].author.id | https://openalex.org/A5056930585 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Selvasundari Subramanian |
| authorships[7].affiliations[0].raw_affiliation_string | Layorz Private Limited, Tamil Nadu, Karur 639117, India |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Selvasundari Subramanian |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Layorz Private Limited, Tamil Nadu, Karur 639117, India |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1424-8220/21/8/2595/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11606 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2205 |
| primary_topic.subfield.display_name | Civil and Structural Engineering |
| primary_topic.display_name | Infrastructure Maintenance and Monitoring |
| related_works | https://openalex.org/W4226493464, https://openalex.org/W3133861977, https://openalex.org/W2951211570, https://openalex.org/W3103566983, https://openalex.org/W2949096641, https://openalex.org/W2969228573, https://openalex.org/W2970686063, https://openalex.org/W4320729701, https://openalex.org/W2963690996, https://openalex.org/W4254103348 |
| cited_by_count | 48 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 10 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 6 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 16 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 7 |
| locations_count | 5 |
| best_oa_location.id | doi:10.3390/s21082595 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1424-8220/21/8/2595/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s21082595 |
| primary_location.id | doi:10.3390/s21082595 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1424-8220/21/8/2595/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s21082595 |
| publication_date | 2021-04-07 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1579553668, https://openalex.org/W2411819132, https://openalex.org/W2601461160, https://openalex.org/W1978969568, https://openalex.org/W3015204527, https://openalex.org/W2981866563, https://openalex.org/W2969181851, https://openalex.org/W2985797574, https://openalex.org/W3134976141, https://openalex.org/W2999717467, https://openalex.org/W2907887410, https://openalex.org/W2994637710, https://openalex.org/W3035791417, https://openalex.org/W3034325995, https://openalex.org/W3005676042, https://openalex.org/W3087776899, https://openalex.org/W3012089510, https://openalex.org/W2890650144, https://openalex.org/W2982682261, https://openalex.org/W3087893031, https://openalex.org/W2966702270, https://openalex.org/W2201079377, https://openalex.org/W2913029471, https://openalex.org/W2970332685, https://openalex.org/W2975407436, https://openalex.org/W2989673213, https://openalex.org/W2992789329, https://openalex.org/W6774416075, https://openalex.org/W2913580266, https://openalex.org/W2884786778, https://openalex.org/W2754556527, https://openalex.org/W2515357728, https://openalex.org/W2554078916, https://openalex.org/W2905297196, https://openalex.org/W2968177401, https://openalex.org/W4237414417, https://openalex.org/W2159986590, https://openalex.org/W6709399668, https://openalex.org/W2293021890, https://openalex.org/W2964308596, https://openalex.org/W3124942917, https://openalex.org/W2407692387, https://openalex.org/W2511065100, https://openalex.org/W3011200270, https://openalex.org/W2920633487, https://openalex.org/W3001104520, https://openalex.org/W3007515473, https://openalex.org/W2748643398, https://openalex.org/W2883318486, https://openalex.org/W2619280051, https://openalex.org/W2787765665, https://openalex.org/W3105550226, https://openalex.org/W3009712583, https://openalex.org/W2620899671 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 40 |
| abstract_inverted_index.It | 142 |
| abstract_inverted_index.be | 26 |
| abstract_inverted_index.by | 30 |
| abstract_inverted_index.in | 153 |
| abstract_inverted_index.is | 11, 73, 143, 149 |
| abstract_inverted_index.of | 93 |
| abstract_inverted_index.on | 134 |
| abstract_inverted_index.or | 19, 125, 161 |
| abstract_inverted_index.to | 57, 75 |
| abstract_inverted_index.GPU | 109 |
| abstract_inverted_index.The | 0, 17, 96, 111, 131 |
| abstract_inverted_index.and | 8, 13, 77, 81, 101, 123, 137 |
| abstract_inverted_index.are | 140 |
| abstract_inverted_index.can | 25 |
| abstract_inverted_index.for | 23, 46, 90, 151, 155 |
| abstract_inverted_index.out | 15, 29 |
| abstract_inverted_index.the | 33, 59, 91, 94, 104, 116, 120, 135, 146 |
| abstract_inverted_index.was | 55, 88, 99 |
| abstract_inverted_index.Deep | 65 |
| abstract_inverted_index.This | 37 |
| abstract_inverted_index.and, | 158 |
| abstract_inverted_index.deep | 41 |
| abstract_inverted_index.from | 62 |
| abstract_inverted_index.high | 129 |
| abstract_inverted_index.that | 115, 145 |
| abstract_inverted_index.used | 74 |
| abstract_inverted_index.with | 32, 103, 128 |
| abstract_inverted_index.work | 38 |
| abstract_inverted_index.(MMS) | 87 |
| abstract_inverted_index.based | 70 |
| abstract_inverted_index.crack | 7, 136 |
| abstract_inverted_index.found | 144 |
| abstract_inverted_index.named | 49 |
| abstract_inverted_index.other | 63 |
| abstract_inverted_index.robot | 48, 106 |
| abstract_inverted_index.task, | 3 |
| abstract_inverted_index.which | 4 |
| abstract_inverted_index.(DCNN) | 69 |
| abstract_inverted_index.Mobile | 84 |
| abstract_inverted_index.NVIDIA | 108 |
| abstract_inverted_index.Neural | 67 |
| abstract_inverted_index.SegNet | 54 |
| abstract_inverted_index.System | 86 |
| abstract_inverted_index.cards. | 110 |
| abstract_inverted_index.detect | 76 |
| abstract_inverted_index.easily | 27 |
| abstract_inverted_index.having | 107 |
| abstract_inverted_index.mainly | 5 |
| abstract_inverted_index.object | 71 |
| abstract_inverted_index.region | 61 |
| abstract_inverted_index.showed | 114 |
| abstract_inverted_index.system | 21, 98 |
| abstract_inverted_index.tasks. | 163 |
| abstract_inverted_index.tested | 102 |
| abstract_inverted_index.Mapping | 85 |
| abstract_inverted_index.Network | 68 |
| abstract_inverted_index.adopted | 56, 89 |
| abstract_inverted_index.carried | 14, 28 |
| abstract_inverted_index.defects | 80, 122 |
| abstract_inverted_index.garbage | 9, 126, 138, 156 |
| abstract_inverted_index.litters | 124 |
| abstract_inverted_index.results | 113, 133 |
| abstract_inverted_index.segment | 58 |
| abstract_inverted_index.Panthera | 105 |
| abstract_inverted_index.Semantic | 51 |
| abstract_inverted_index.approach | 22 |
| abstract_inverted_index.cleaning | 162 |
| abstract_inverted_index.defects. | 95 |
| abstract_inverted_index.garbage. | 82 |
| abstract_inverted_index.includes | 6 |
| abstract_inverted_index.localize | 78 |
| abstract_inverted_index.objects. | 64 |
| abstract_inverted_index.pavement | 1, 34, 43, 60, 79, 121 |
| abstract_inverted_index.proposed | 97, 117, 147 |
| abstract_inverted_index.proposes | 39 |
| abstract_inverted_index.suitable | 150 |
| abstract_inverted_index.sweeping | 35, 160 |
| abstract_inverted_index.Panthera. | 50 |
| abstract_inverted_index.accuracy. | 130 |
| abstract_inverted_index.dedicated | 20 |
| abstract_inverted_index.detection | 72, 127, 139, 157 |
| abstract_inverted_index.essential | 12 |
| abstract_inverted_index.framework | 45, 53 |
| abstract_inverted_index.machines. | 36 |
| abstract_inverted_index.real-time | 154 |
| abstract_inverted_index.technique | 118, 148 |
| abstract_inverted_index.deployment | 152 |
| abstract_inverted_index.detection, | 10 |
| abstract_inverted_index.geotagging | 92 |
| abstract_inverted_index.identifies | 119 |
| abstract_inverted_index.inspection | 2, 24, 44 |
| abstract_inverted_index.presented. | 141 |
| abstract_inverted_index.eventually, | 159 |
| abstract_inverted_index.frequently. | 16 |
| abstract_inverted_index.human-based | 18 |
| abstract_inverted_index.implemented | 100 |
| abstract_inverted_index.integrating | 31 |
| abstract_inverted_index.Furthermore, | 83 |
| abstract_inverted_index.experimental | 112, 132 |
| abstract_inverted_index.segmentation | 52 |
| abstract_inverted_index.Convolutional | 66 |
| abstract_inverted_index.learning-based | 42 |
| abstract_inverted_index.self-reconfigurable | 47 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5057929288 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I152815399 |
| citation_normalized_percentile.value | 0.9619913 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |