A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning Article Swipe
Rong Meng
,
Zhao-lei Wang
,
Zhilong Zhao
,
Jianpeng Li
,
Weiping Fu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/1188617
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/1188617
Aiming at the problem of multi-modal fault detection of different equipment in ultrahigh voltage (UHV) substations, a method for based on robot inspection and deep learning is proposed. First, the inspection robot is used to collect the image data of different devices in the station and the source data is preprocessed by standard image augmentation and image aliasing augmentation. Then, the HSV color space model based on saliency area detection is used to extract equipment defect areas, which improves the accuracy of defect image classification. Finally, the traditional YOLOv3 network is improved by combining the residual network and the K-means clustering algorithm, and the detailed flow of the corresponding detection method is proposed. The proposed detection method and the other three methods were compared and analyzed under the same conditions through simulation experiments. The results show that the detection accuracy and recall rate of the method proposed in this study are the largest, which are 95.9% and 91.3%, respectively. The average detection accuracy under multiple intersection ratio thresholds is also the highest, and the performance is better than the other three comparison algorithms.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/1188617
- https://downloads.hindawi.com/journals/jr/2022/1188617.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224256485
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4224256485Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/1188617Digital Object Identifier
- Title
-
A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-23Full publication date if available
- Authors
-
Rong Meng, Zhao-lei Wang, Zhilong Zhao, Jianpeng Li, Weiping FuList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/1188617Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/jr/2022/1188617.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://downloads.hindawi.com/journals/jr/2022/1188617.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Intersection (aeronautics), Computer vision, Deep learning, Residual, Robot, Fault detection and isolation, Aliasing, Cluster analysis, Pattern recognition (psychology), Algorithm, Aerospace engineering, Undersampling, Actuator, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
7Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4224256485 |
|---|---|
| doi | https://doi.org/10.1155/2022/1188617 |
| ids.doi | https://doi.org/10.1155/2022/1188617 |
| ids.openalex | https://openalex.org/W4224256485 |
| fwci | 0.24729856 |
| type | article |
| title | A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning |
| awards[0].id | https://openalex.org/G8317910953 |
| awards[0].funder_id | https://openalex.org/F4320326707 |
| awards[0].display_name | |
| awards[0].funder_award_id | kj2021-059 |
| awards[0].funder_display_name | State Grid Corporation of China |
| biblio.issue | |
| biblio.volume | 2022 |
| biblio.last_page | 9 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T13715 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9975000023841858 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2210 |
| topics[0].subfield.display_name | Mechanical Engineering |
| topics[0].display_name | Power Line Inspection Robots |
| topics[1].id | https://openalex.org/T10036 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9868999719619751 |
| 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 | Advanced Neural Network Applications |
| topics[2].id | https://openalex.org/T13918 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9562000036239624 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Advanced Data and IoT Technologies |
| funders[0].id | https://openalex.org/F4320326707 |
| funders[0].ror | https://ror.org/05twwhs70 |
| funders[0].display_name | State Grid Corporation of China |
| is_xpac | False |
| apc_list.value | 900 |
| apc_list.currency | USD |
| apc_list.value_usd | 900 |
| apc_paid.value | 900 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 900 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8185467720031738 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7479315996170044 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C64543145 |
| concepts[2].level | 2 |
| concepts[2].score | 0.590152382850647 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q162942 |
| concepts[2].display_name | Intersection (aeronautics) |
| concepts[3].id | https://openalex.org/C31972630 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5147473812103271 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[3].display_name | Computer vision |
| concepts[4].id | https://openalex.org/C108583219 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5104678869247437 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[4].display_name | Deep learning |
| concepts[5].id | https://openalex.org/C155512373 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5067175030708313 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[5].display_name | Residual |
| concepts[6].id | https://openalex.org/C90509273 |
| concepts[6].level | 2 |
| concepts[6].score | 0.502844512462616 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11012 |
| concepts[6].display_name | Robot |
| concepts[7].id | https://openalex.org/C152745839 |
| concepts[7].level | 3 |
| concepts[7].score | 0.4926627278327942 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q5438153 |
| concepts[7].display_name | Fault detection and isolation |
| concepts[8].id | https://openalex.org/C4069607 |
| concepts[8].level | 3 |
| concepts[8].score | 0.45896151661872864 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q868732 |
| concepts[8].display_name | Aliasing |
| concepts[9].id | https://openalex.org/C73555534 |
| concepts[9].level | 2 |
| concepts[9].score | 0.41024696826934814 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[9].display_name | Cluster analysis |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3888099789619446 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C11413529 |
| concepts[11].level | 1 |
| concepts[11].score | 0.12242823839187622 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[11].display_name | Algorithm |
| concepts[12].id | https://openalex.org/C146978453 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[12].display_name | Aerospace engineering |
| concepts[13].id | https://openalex.org/C136536468 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1225894 |
| concepts[13].display_name | Undersampling |
| concepts[14].id | https://openalex.org/C172707124 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q423488 |
| concepts[14].display_name | Actuator |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8185467720031738 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7479315996170044 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/intersection |
| keywords[2].score | 0.590152382850647 |
| keywords[2].display_name | Intersection (aeronautics) |
| keywords[3].id | https://openalex.org/keywords/computer-vision |
| keywords[3].score | 0.5147473812103271 |
| keywords[3].display_name | Computer vision |
| keywords[4].id | https://openalex.org/keywords/deep-learning |
| keywords[4].score | 0.5104678869247437 |
| keywords[4].display_name | Deep learning |
| keywords[5].id | https://openalex.org/keywords/residual |
| keywords[5].score | 0.5067175030708313 |
| keywords[5].display_name | Residual |
| keywords[6].id | https://openalex.org/keywords/robot |
| keywords[6].score | 0.502844512462616 |
| keywords[6].display_name | Robot |
| keywords[7].id | https://openalex.org/keywords/fault-detection-and-isolation |
| keywords[7].score | 0.4926627278327942 |
| keywords[7].display_name | Fault detection and isolation |
| keywords[8].id | https://openalex.org/keywords/aliasing |
| keywords[8].score | 0.45896151661872864 |
| keywords[8].display_name | Aliasing |
| keywords[9].id | https://openalex.org/keywords/cluster-analysis |
| keywords[9].score | 0.41024696826934814 |
| keywords[9].display_name | Cluster analysis |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.3888099789619446 |
| keywords[10].display_name | Pattern recognition (psychology) |
| keywords[11].id | https://openalex.org/keywords/algorithm |
| keywords[11].score | 0.12242823839187622 |
| keywords[11].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.1155/2022/1188617 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S176211327 |
| locations[0].source.issn | 1687-9600, 1687-9619 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1687-9600 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Journal of Robotics |
| locations[0].source.host_organization | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_name | Hindawi Publishing Corporation |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319869 |
| locations[0].source.host_organization_lineage_names | Hindawi Publishing Corporation |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://downloads.hindawi.com/journals/jr/2022/1188617.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 | Journal of Robotics |
| locations[0].landing_page_url | https://doi.org/10.1155/2022/1188617 |
| locations[1].id | pmh:oai:doaj.org/article:5517c22758b74364ac6708510978d073 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Journal of Robotics, Vol 2022 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/5517c22758b74364ac6708510978d073 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5102810561 |
| authorships[0].author.orcid | https://orcid.org/0009-0009-2172-1919 |
| authorships[0].author.display_name | Rong Meng |
| authorships[0].affiliations[0].raw_affiliation_string | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Rong Meng |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[1].author.id | https://openalex.org/A5079847483 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4645-8941 |
| authorships[1].author.display_name | Zhao-lei Wang |
| authorships[1].affiliations[0].raw_affiliation_string | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhao-lei Wang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[2].author.id | https://openalex.org/A5100775937 |
| authorships[2].author.orcid | https://orcid.org/0009-0004-1698-3942 |
| authorships[2].author.display_name | Zhilong Zhao |
| authorships[2].affiliations[0].raw_affiliation_string | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhi-long Zhao |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[3].author.id | https://openalex.org/A5100772470 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4518-9655 |
| authorships[3].author.display_name | Jianpeng Li |
| authorships[3].affiliations[0].raw_affiliation_string | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jian-peng Li |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[4].author.id | https://openalex.org/A5101938303 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-9803-3908 |
| authorships[4].author.display_name | Weiping Fu |
| authorships[4].affiliations[0].raw_affiliation_string | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Wei-ping Fu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | State Grid Hebei Extra High Voltage Company, Shijiazhuang, Heibei, 050070, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://downloads.hindawi.com/journals/jr/2022/1188617.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13715 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9975000023841858 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2210 |
| primary_topic.subfield.display_name | Mechanical Engineering |
| primary_topic.display_name | Power Line Inspection Robots |
| related_works | https://openalex.org/W2560215812, https://openalex.org/W2949601986, https://openalex.org/W2348909947, https://openalex.org/W4375867731, https://openalex.org/W2788972299, https://openalex.org/W4292672442, https://openalex.org/W4385301753, https://openalex.org/W3186428265, https://openalex.org/W2171501125, https://openalex.org/W2617234683 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1155/2022/1188617 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S176211327 |
| best_oa_location.source.issn | 1687-9600, 1687-9619 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1687-9600 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Journal of Robotics |
| best_oa_location.source.host_organization | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_name | Hindawi Publishing Corporation |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| best_oa_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://downloads.hindawi.com/journals/jr/2022/1188617.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 | Journal of Robotics |
| best_oa_location.landing_page_url | https://doi.org/10.1155/2022/1188617 |
| primary_location.id | doi:10.1155/2022/1188617 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S176211327 |
| primary_location.source.issn | 1687-9600, 1687-9619 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1687-9600 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Journal of Robotics |
| primary_location.source.host_organization | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_name | Hindawi Publishing Corporation |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319869 |
| primary_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://downloads.hindawi.com/journals/jr/2022/1188617.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 | Journal of Robotics |
| primary_location.landing_page_url | https://doi.org/10.1155/2022/1188617 |
| publication_date | 2022-04-23 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2959781444, https://openalex.org/W3006469850, https://openalex.org/W2740697227, https://openalex.org/W3021665978, https://openalex.org/W3135387813, https://openalex.org/W3049107186, https://openalex.org/W2945625514 |
| referenced_works_count | 7 |
| abstract_inverted_index.a | 16 |
| abstract_inverted_index.at | 1 |
| abstract_inverted_index.by | 51, 92 |
| abstract_inverted_index.in | 11, 42, 147 |
| abstract_inverted_index.is | 26, 32, 49, 70, 90, 111, 168, 175 |
| abstract_inverted_index.of | 4, 8, 39, 81, 106, 143 |
| abstract_inverted_index.on | 20, 66 |
| abstract_inverted_index.to | 34, 72 |
| abstract_inverted_index.HSV | 61 |
| abstract_inverted_index.The | 113, 133, 159 |
| abstract_inverted_index.and | 23, 45, 55, 97, 102, 117, 124, 140, 156, 172 |
| abstract_inverted_index.are | 150, 154 |
| abstract_inverted_index.for | 18 |
| abstract_inverted_index.the | 2, 29, 36, 43, 46, 60, 79, 86, 94, 98, 103, 107, 118, 127, 137, 144, 151, 170, 173, 178 |
| abstract_inverted_index.also | 169 |
| abstract_inverted_index.area | 68 |
| abstract_inverted_index.data | 38, 48 |
| abstract_inverted_index.deep | 24 |
| abstract_inverted_index.flow | 105 |
| abstract_inverted_index.rate | 142 |
| abstract_inverted_index.same | 128 |
| abstract_inverted_index.show | 135 |
| abstract_inverted_index.than | 177 |
| abstract_inverted_index.that | 136 |
| abstract_inverted_index.this | 148 |
| abstract_inverted_index.used | 33, 71 |
| abstract_inverted_index.were | 122 |
| abstract_inverted_index.(UHV) | 14 |
| abstract_inverted_index.95.9% | 155 |
| abstract_inverted_index.Then, | 59 |
| abstract_inverted_index.based | 19, 65 |
| abstract_inverted_index.color | 62 |
| abstract_inverted_index.fault | 6 |
| abstract_inverted_index.image | 37, 53, 56, 83 |
| abstract_inverted_index.model | 64 |
| abstract_inverted_index.other | 119, 179 |
| abstract_inverted_index.ratio | 166 |
| abstract_inverted_index.robot | 21, 31 |
| abstract_inverted_index.space | 63 |
| abstract_inverted_index.study | 149 |
| abstract_inverted_index.three | 120, 180 |
| abstract_inverted_index.under | 126, 163 |
| abstract_inverted_index.which | 77, 153 |
| abstract_inverted_index.91.3%, | 157 |
| abstract_inverted_index.Aiming | 0 |
| abstract_inverted_index.First, | 28 |
| abstract_inverted_index.YOLOv3 | 88 |
| abstract_inverted_index.areas, | 76 |
| abstract_inverted_index.better | 176 |
| abstract_inverted_index.defect | 75, 82 |
| abstract_inverted_index.method | 17, 110, 116, 145 |
| abstract_inverted_index.recall | 141 |
| abstract_inverted_index.source | 47 |
| abstract_inverted_index.K-means | 99 |
| abstract_inverted_index.average | 160 |
| abstract_inverted_index.collect | 35 |
| abstract_inverted_index.devices | 41 |
| abstract_inverted_index.extract | 73 |
| abstract_inverted_index.methods | 121 |
| abstract_inverted_index.network | 89, 96 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.results | 134 |
| abstract_inverted_index.station | 44 |
| abstract_inverted_index.through | 130 |
| abstract_inverted_index.voltage | 13 |
| abstract_inverted_index.Finally, | 85 |
| abstract_inverted_index.accuracy | 80, 139, 162 |
| abstract_inverted_index.aliasing | 57 |
| abstract_inverted_index.analyzed | 125 |
| abstract_inverted_index.compared | 123 |
| abstract_inverted_index.detailed | 104 |
| abstract_inverted_index.highest, | 171 |
| abstract_inverted_index.improved | 91 |
| abstract_inverted_index.improves | 78 |
| abstract_inverted_index.largest, | 152 |
| abstract_inverted_index.learning | 25 |
| abstract_inverted_index.multiple | 164 |
| abstract_inverted_index.proposed | 114, 146 |
| abstract_inverted_index.residual | 95 |
| abstract_inverted_index.saliency | 67 |
| abstract_inverted_index.standard | 52 |
| abstract_inverted_index.combining | 93 |
| abstract_inverted_index.detection | 7, 69, 109, 115, 138, 161 |
| abstract_inverted_index.different | 9, 40 |
| abstract_inverted_index.equipment | 10, 74 |
| abstract_inverted_index.proposed. | 27, 112 |
| abstract_inverted_index.ultrahigh | 12 |
| abstract_inverted_index.algorithm, | 101 |
| abstract_inverted_index.clustering | 100 |
| abstract_inverted_index.comparison | 181 |
| abstract_inverted_index.conditions | 129 |
| abstract_inverted_index.inspection | 22, 30 |
| abstract_inverted_index.simulation | 131 |
| abstract_inverted_index.thresholds | 167 |
| abstract_inverted_index.algorithms. | 182 |
| abstract_inverted_index.multi-modal | 5 |
| abstract_inverted_index.performance | 174 |
| abstract_inverted_index.traditional | 87 |
| abstract_inverted_index.augmentation | 54 |
| abstract_inverted_index.experiments. | 132 |
| abstract_inverted_index.intersection | 165 |
| abstract_inverted_index.preprocessed | 50 |
| abstract_inverted_index.substations, | 15 |
| abstract_inverted_index.augmentation. | 58 |
| abstract_inverted_index.corresponding | 108 |
| abstract_inverted_index.respectively. | 158 |
| abstract_inverted_index.classification. | 84 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5079847483 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.44079064 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |