Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation.
<p>Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852018368616333312 |
|---|---|
| author | Persiya J. (21743388) |
| author2 | Sasithradevi A (21743391) |
| author2_role | author |
| author_facet | Persiya J. (21743388) Sasithradevi A (21743391) |
| author_role | author |
| dc.creator.none.fl_str_mv | Persiya J. (21743388) Sasithradevi A (21743391) |
| dc.date.none.fl_str_mv | 2025-07-18T17:26:49Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0328227.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Analyzing_the_Impact_of_CLAHE_parameter_Variations_on_Different_YOLO_Models_on_the_dataset_without_Augmentation_/29599935 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified subjective quality measures improve image clarity detected eye regions enhanced images achieved accurate eye detection investigate various super image processing techniques advanced yolo models eye detection yolo models thermal images advanced super work demonstrates work addresses unique dataset thermal data synergistic fusion proposed pipeline often hindered multifaceted approach integrated pipeline inherent limitations including biometrics first introduce experiments reveal evaluated using driver monitoring diverse applications deep learning computer interaction 801 ), |
| dc.title.none.fl_str_mv | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a08ea40c393d12e050fdcf178f2bce95 |
| identifier_str_mv | 10.1371/journal.pone.0328227.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29599935 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation.Persiya J. (21743388)Sasithradevi A (21743391)BiotechnologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsubjective quality measuresimprove image claritydetected eye regionsenhanced images achievedaccurate eye detectioninvestigate various superimage processing techniquesadvanced yolo modelseye detectionyolo modelsthermal imagesadvanced superwork demonstrateswork addressesunique datasetthermal datasynergistic fusionproposed pipelineoften hinderedmultifaceted approachintegrated pipelineinherent limitationsincluding biometricsfirst introduceexperiments revealevaluated usingdriver monitoringdiverse applicationsdeep learningcomputer interaction801 ),<p>Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation.</p>2025-07-18T17:26:49ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0328227.t003https://figshare.com/articles/dataset/Analyzing_the_Impact_of_CLAHE_parameter_Variations_on_Different_YOLO_Models_on_the_dataset_without_Augmentation_/29599935CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/295999352025-07-18T17:26:49Z |
| spellingShingle | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. Persiya J. (21743388) Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified subjective quality measures improve image clarity detected eye regions enhanced images achieved accurate eye detection investigate various super image processing techniques advanced yolo models eye detection yolo models thermal images advanced super work demonstrates work addresses unique dataset thermal data synergistic fusion proposed pipeline often hindered multifaceted approach integrated pipeline inherent limitations including biometrics first introduce experiments reveal evaluated using driver monitoring diverse applications deep learning computer interaction 801 ), |
| status_str | publishedVersion |
| title | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| title_full | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| title_fullStr | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| title_full_unstemmed | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| title_short | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| title_sort | Analyzing the Impact of CLAHE parameter Variations on Different YOLO Models on the dataset without Augmentation. |
| topic | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified subjective quality measures improve image clarity detected eye regions enhanced images achieved accurate eye detection investigate various super image processing techniques advanced yolo models eye detection yolo models thermal images advanced super work demonstrates work addresses unique dataset thermal data synergistic fusion proposed pipeline often hindered multifaceted approach integrated pipeline inherent limitations including biometrics first introduce experiments reveal evaluated using driver monitoring diverse applications deep learning computer interaction 801 ), |