MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images Article Swipe
Shengbang Zhou
,
Song Zhang
,
Chuanqi Li
,
Shutian Liu
,
Dong Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25113468
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25113468
Due to complex backgrounds, significant scale variations of targets, and dense distributions of small objects in Unmanned Aerial Vehicle (UAV) aerial images, traditional object detection algorithms face challenges in adapting to such scenarios. This article introduces a drone detection model, MEP-YOLOv5s, which optimizes the Backbone, Neck layer, and C3 module based on YOLOv5s, and combines effective attention mechanisms to improve the training efficiency of the model by replacing the traditional CIoU loss (Complete Intersection over Union) with MPDIoU (Minimum Point Distance-based Intersection over Union) loss. This model demonstrates an excellent performance in handling typical drone detection scenarios, especially for small and dense objects. To holistically balance the detection accuracy and inference efficiency, we propose a Comprehensive Performance Indicator (CPI), which evaluates the model performance by considering both accuracy and efficiency. Evaluations on the VisDrone2019 dataset demonstrate that MEP-YOLOv5s achieves a 3.3% improvement in precision (P), a 20.9% increase in [email protected], and a 19.86% gain in the CPI (α = 0.5) compared with the baseline model. Additional experiments on the NWPU VHR-10 dataset confirm that MEP-YOLOv5s outperforms the existing state-of-the-art methods, offering a robust solution for UAV-based small object detection with enhanced feature extraction and attention-driven adaptability.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25113468
- https://www.mdpi.com/1424-8220/25/11/3468/pdf?version=1748617898
- OA Status
- gold
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410960540
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410960540Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s25113468Digital Object Identifier
- Title
-
MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-30Full publication date if available
- Authors
-
Shengbang Zhou, Song Zhang, Chuanqi Li, Shutian Liu, Dong ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/s25113468Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/25/11/3468/pdf?version=1748617898Direct 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/1424-8220/25/11/3468/pdf?version=1748617898Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Computer vision, Remote sensing, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4410960540 |
|---|---|
| doi | https://doi.org/10.3390/s25113468 |
| ids.doi | https://doi.org/10.3390/s25113468 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40969026 |
| ids.openalex | https://openalex.org/W4410960540 |
| fwci | 0.0 |
| type | article |
| title | MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images |
| biblio.issue | 11 |
| biblio.volume | 25 |
| biblio.last_page | 3468 |
| biblio.first_page | 3468 |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T11133 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9934999942779541 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | UAV Applications and Optimization |
| topics[2].id | https://openalex.org/T10191 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9916999936103821 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2202 |
| topics[2].subfield.display_name | Aerospace Engineering |
| topics[2].display_name | Robotics and Sensor-Based Localization |
| 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/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.5376710891723633 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5371696352958679 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C31972630 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5104941129684448 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[2].display_name | Computer vision |
| concepts[3].id | https://openalex.org/C62649853 |
| concepts[3].level | 1 |
| concepts[3].score | 0.45056477189064026 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[3].display_name | Remote sensing |
| concepts[4].id | https://openalex.org/C205649164 |
| concepts[4].level | 0 |
| concepts[4].score | 0.18623998761177063 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[4].display_name | Geography |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.5376710891723633 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5371696352958679 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/computer-vision |
| keywords[2].score | 0.5104941129684448 |
| keywords[2].display_name | Computer vision |
| keywords[3].id | https://openalex.org/keywords/remote-sensing |
| keywords[3].score | 0.45056477189064026 |
| keywords[3].display_name | Remote sensing |
| keywords[4].id | https://openalex.org/keywords/geography |
| keywords[4].score | 0.18623998761177063 |
| keywords[4].display_name | Geography |
| language | en |
| locations[0].id | doi:10.3390/s25113468 |
| 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/1424-8220/25/11/3468/pdf?version=1748617898 |
| 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/s25113468 |
| locations[1].id | pmid:40969026 |
| 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/40969026 |
| locations[2].id | pmh:oai:doaj.org/article:9917508fdfcf43d9a8c14cba7d138e5f |
| locations[2].is_oa | False |
| 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 | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 25, Iss 11, p 3468 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/9917508fdfcf43d9a8c14cba7d138e5f |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:12158283 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sensors (Basel) |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12158283 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5017944261 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Shengbang Zhou |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210151929 |
| authorships[0].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210151929 |
| authorships[0].institutions[0].ror | https://ror.org/04dx82x73 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210151929 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Nanning Normal University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shengbang Zhou |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[1].author.id | https://openalex.org/A5100366539 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4893-9565 |
| authorships[1].author.display_name | Song Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210151929 |
| authorships[1].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210151929 |
| authorships[1].institutions[0].ror | https://ror.org/04dx82x73 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210151929 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Nanning Normal University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Song Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[2].author.id | https://openalex.org/A5041698589 |
| authorships[2].author.orcid | https://orcid.org/0009-0003-4503-0625 |
| authorships[2].author.display_name | Chuanqi Li |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210151929 |
| authorships[2].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210151929 |
| authorships[2].institutions[0].ror | https://ror.org/04dx82x73 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210151929 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Nanning Normal University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Chuanqi Li |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[3].author.id | https://openalex.org/A5101983961 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9748-0175 |
| authorships[3].author.display_name | Shutian Liu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210151929 |
| authorships[3].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210151929 |
| authorships[3].institutions[0].ror | https://ror.org/04dx82x73 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210151929 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Nanning Normal University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Shutian Liu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China |
| authorships[4].author.id | https://openalex.org/A5111273410 |
| authorships[4].author.orcid | https://orcid.org/0009-0004-8928-3322 |
| authorships[4].author.display_name | Dong Chen |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210151929 |
| authorships[4].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210151929 |
| authorships[4].institutions[0].ror | https://ror.org/04dx82x73 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210151929 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Nanning Normal University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Dong Chen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China |
| 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/1424-8220/25/11/3468/pdf?version=1748617898 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | MEP-YOLOv5s: Small-Target Detection Model for Unmanned Aerial Vehicle-Captured Images |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407, https://openalex.org/W2079911747, https://openalex.org/W1969923398 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3390/s25113468 |
| 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/1424-8220/25/11/3468/pdf?version=1748617898 |
| 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/s25113468 |
| primary_location.id | doi:10.3390/s25113468 |
| 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/1424-8220/25/11/3468/pdf?version=1748617898 |
| 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/s25113468 |
| publication_date | 2025-05-30 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4210666108, https://openalex.org/W3008118860, https://openalex.org/W2947616598, https://openalex.org/W3091557600, https://openalex.org/W4360995420, https://openalex.org/W4382766106, https://openalex.org/W2164598857, https://openalex.org/W6683411478, https://openalex.org/W2120419212, https://openalex.org/W2786558633, https://openalex.org/W2963150697, https://openalex.org/W2964241181, https://openalex.org/W4382059335, https://openalex.org/W4324061701, https://openalex.org/W4386076325, https://openalex.org/W2193145675, https://openalex.org/W2919011445, https://openalex.org/W4386913701, https://openalex.org/W4380081596, https://openalex.org/W4392191088, https://openalex.org/W4388795404, https://openalex.org/W4382402702, https://openalex.org/W4396592538, https://openalex.org/W4382133590, https://openalex.org/W4391985970, https://openalex.org/W4367396192, https://openalex.org/W4386157925, https://openalex.org/W4395660071, https://openalex.org/W4372347372, https://openalex.org/W3042011474, https://openalex.org/W2997747012, https://openalex.org/W4303699847, https://openalex.org/W4386307058, https://openalex.org/W3106250896 |
| referenced_works_count | 34 |
| abstract_inverted_index.= | 158 |
| abstract_inverted_index.a | 36, 114, 139, 145, 151, 181 |
| abstract_inverted_index.C3 | 48 |
| abstract_inverted_index.To | 103 |
| abstract_inverted_index.an | 88 |
| abstract_inverted_index.by | 66, 124 |
| abstract_inverted_index.in | 15, 28, 91, 142, 148, 154 |
| abstract_inverted_index.of | 7, 12, 63 |
| abstract_inverted_index.on | 51, 131, 167 |
| abstract_inverted_index.to | 1, 30, 58 |
| abstract_inverted_index.we | 112 |
| abstract_inverted_index.(α | 157 |
| abstract_inverted_index.CPI | 156 |
| abstract_inverted_index.Due | 0 |
| abstract_inverted_index.and | 9, 47, 53, 100, 109, 128, 150, 193 |
| abstract_inverted_index.for | 98, 184 |
| abstract_inverted_index.the | 43, 60, 64, 68, 106, 121, 132, 155, 162, 168, 176 |
| abstract_inverted_index.(P), | 144 |
| abstract_inverted_index.0.5) | 159 |
| abstract_inverted_index.3.3% | 140 |
| abstract_inverted_index.CIoU | 70 |
| abstract_inverted_index.NWPU | 169 |
| abstract_inverted_index.Neck | 45 |
| abstract_inverted_index.This | 33, 85 |
| abstract_inverted_index.both | 126 |
| abstract_inverted_index.face | 26 |
| abstract_inverted_index.gain | 153 |
| abstract_inverted_index.loss | 71 |
| abstract_inverted_index.over | 74, 82 |
| abstract_inverted_index.such | 31 |
| abstract_inverted_index.that | 136, 173 |
| abstract_inverted_index.with | 76, 161, 189 |
| abstract_inverted_index.(UAV) | 19 |
| abstract_inverted_index.20.9% | 146 |
| abstract_inverted_index.Point | 79 |
| abstract_inverted_index.based | 50 |
| abstract_inverted_index.dense | 10, 101 |
| abstract_inverted_index.drone | 37, 94 |
| abstract_inverted_index.loss. | 84 |
| abstract_inverted_index.model | 65, 86, 122 |
| abstract_inverted_index.scale | 5 |
| abstract_inverted_index.small | 13, 99, 186 |
| abstract_inverted_index.which | 41, 119 |
| abstract_inverted_index.(CPI), | 118 |
| abstract_inverted_index.19.86% | 152 |
| abstract_inverted_index.Aerial | 17 |
| abstract_inverted_index.MPDIoU | 77 |
| abstract_inverted_index.Union) | 75, 83 |
| abstract_inverted_index.VHR-10 | 170 |
| abstract_inverted_index.aerial | 20 |
| abstract_inverted_index.layer, | 46 |
| abstract_inverted_index.model, | 39 |
| abstract_inverted_index.model. | 164 |
| abstract_inverted_index.module | 49 |
| abstract_inverted_index.object | 23, 187 |
| abstract_inverted_index.robust | 182 |
| abstract_inverted_index.Vehicle | 18 |
| abstract_inverted_index.article | 34 |
| abstract_inverted_index.balance | 105 |
| abstract_inverted_index.complex | 2 |
| abstract_inverted_index.confirm | 172 |
| abstract_inverted_index.dataset | 134, 171 |
| abstract_inverted_index.feature | 191 |
| abstract_inverted_index.images, | 21 |
| abstract_inverted_index.improve | 59 |
| abstract_inverted_index.objects | 14 |
| abstract_inverted_index.propose | 113 |
| abstract_inverted_index.typical | 93 |
| abstract_inverted_index.(Minimum | 78 |
| abstract_inverted_index.Unmanned | 16 |
| abstract_inverted_index.YOLOv5s, | 52 |
| abstract_inverted_index.accuracy | 108, 127 |
| abstract_inverted_index.achieves | 138 |
| abstract_inverted_index.adapting | 29 |
| abstract_inverted_index.baseline | 163 |
| abstract_inverted_index.combines | 54 |
| abstract_inverted_index.compared | 160 |
| abstract_inverted_index.enhanced | 190 |
| abstract_inverted_index.existing | 177 |
| abstract_inverted_index.handling | 92 |
| abstract_inverted_index.increase | 147 |
| [email protected], | 149 |
| abstract_inverted_index.methods, | 179 |
| abstract_inverted_index.objects. | 102 |
| abstract_inverted_index.offering | 180 |
| abstract_inverted_index.solution | 183 |
| abstract_inverted_index.targets, | 8 |
| abstract_inverted_index.training | 61 |
| abstract_inverted_index.(Complete | 72 |
| abstract_inverted_index.Backbone, | 44 |
| abstract_inverted_index.Indicator | 117 |
| abstract_inverted_index.UAV-based | 185 |
| abstract_inverted_index.attention | 56 |
| abstract_inverted_index.detection | 24, 38, 95, 107, 188 |
| abstract_inverted_index.effective | 55 |
| abstract_inverted_index.evaluates | 120 |
| abstract_inverted_index.excellent | 89 |
| abstract_inverted_index.inference | 110 |
| abstract_inverted_index.optimizes | 42 |
| abstract_inverted_index.precision | 143 |
| abstract_inverted_index.replacing | 67 |
| abstract_inverted_index.Additional | 165 |
| abstract_inverted_index.algorithms | 25 |
| abstract_inverted_index.challenges | 27 |
| abstract_inverted_index.efficiency | 62 |
| abstract_inverted_index.especially | 97 |
| abstract_inverted_index.extraction | 192 |
| abstract_inverted_index.introduces | 35 |
| abstract_inverted_index.mechanisms | 57 |
| abstract_inverted_index.scenarios, | 96 |
| abstract_inverted_index.scenarios. | 32 |
| abstract_inverted_index.variations | 6 |
| abstract_inverted_index.Evaluations | 130 |
| abstract_inverted_index.MEP-YOLOv5s | 137, 174 |
| abstract_inverted_index.Performance | 116 |
| abstract_inverted_index.considering | 125 |
| abstract_inverted_index.demonstrate | 135 |
| abstract_inverted_index.efficiency, | 111 |
| abstract_inverted_index.efficiency. | 129 |
| abstract_inverted_index.experiments | 166 |
| abstract_inverted_index.improvement | 141 |
| abstract_inverted_index.outperforms | 175 |
| abstract_inverted_index.performance | 90, 123 |
| abstract_inverted_index.significant | 4 |
| abstract_inverted_index.traditional | 22, 69 |
| abstract_inverted_index.Intersection | 73, 81 |
| abstract_inverted_index.MEP-YOLOv5s, | 40 |
| abstract_inverted_index.VisDrone2019 | 133 |
| abstract_inverted_index.backgrounds, | 3 |
| abstract_inverted_index.demonstrates | 87 |
| abstract_inverted_index.holistically | 104 |
| abstract_inverted_index.Comprehensive | 115 |
| abstract_inverted_index.adaptability. | 195 |
| abstract_inverted_index.distributions | 11 |
| abstract_inverted_index.Distance-based | 80 |
| abstract_inverted_index.attention-driven | 194 |
| abstract_inverted_index.state-of-the-art | 178 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.19798334 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |