Confusion matrix of the model.
<div><p>The rapid population growth in urban areas has led to an increased frequency of lost and unclaimed items in public spaces such as public transportation, restaurants, and other venues. Services like Find My iPhone efficiently track lost electronic devices, but many valuable items...
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2024
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| _version_ | 1852025552176676864 |
|---|---|
| author | Meihua Zhou (12331454) |
| author2 | Ivan Fung (13814059) Li Yang (6520) Nan Wan (4356544) Keke Di (19976488) Tingting Wang (123983) |
| author2_role | author author author author author |
| author_facet | Meihua Zhou (12331454) Ivan Fung (13814059) Li Yang (6520) Nan Wan (4356544) Keke Di (19976488) Tingting Wang (123983) |
| author_role | author |
| dc.creator.none.fl_str_mv | Meihua Zhou (12331454) Ivan Fung (13814059) Li Yang (6520) Nan Wan (4356544) Keke Di (19976488) Tingting Wang (123983) |
| dc.date.none.fl_str_mv | 2024-10-30T17:26:21Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0310998.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Confusion_matrix_of_the_model_/27345133 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified transfer learning capabilities services like find rapid population growth photo matching network internet framework based 8 %, utilizing 5m training parameters recovered items provided find </ p xlink "> world scenarios urban areas unclaimed items thus enabling testing accuracy smart way search process research presents public transportation public spaces photos taken increased frequency implementation achieves 67 gflops |
| dc.title.none.fl_str_mv | Confusion matrix of the model. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>The rapid population growth in urban areas has led to an increased frequency of lost and unclaimed items in public spaces such as public transportation, restaurants, and other venues. Services like Find My iPhone efficiently track lost electronic devices, but many valuable items remain unmonitored, resulting in delays in reclaiming lost and found items. This research presents a method to streamline the search process by comparing images of lost and recovered items provided by owners with photos taken when items are registered as lost and found. A photo matching network is proposed, integrating the transfer learning capabilities of MobileNetV2 with the Convolutional Block Attention Module (CBAM) and utilizing perceptual hashing algorithms for their simplicity and speed. An Internet framework based on the Spring Boot system supports the development of an online lost and found image identification system. The implementation achieves a testing accuracy of 96.8%, utilizing only 0.67 GFLOPs and 3.5M training parameters, thus enabling the recognition of images in real-world scenarios and operable on standard laptops.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_11c1683eeae00a27c65ebc0eaf8ca0ce |
| identifier_str_mv | 10.1371/journal.pone.0310998.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27345133 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Confusion matrix of the model.Meihua Zhou (12331454)Ivan Fung (13814059)Li Yang (6520)Nan Wan (4356544)Keke Di (19976488)Tingting Wang (123983)MedicineSpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtransfer learning capabilitiesservices like findrapid population growthphoto matching networkinternet framework based8 %, utilizing5m training parametersrecovered items providedfind </ pxlink ">world scenariosurban areasunclaimed itemsthus enablingtesting accuracysmart waysearch processresearch presentspublic transportationpublic spacesphotos takenincreased frequencyimplementation achieves67 gflops<div><p>The rapid population growth in urban areas has led to an increased frequency of lost and unclaimed items in public spaces such as public transportation, restaurants, and other venues. Services like Find My iPhone efficiently track lost electronic devices, but many valuable items remain unmonitored, resulting in delays in reclaiming lost and found items. This research presents a method to streamline the search process by comparing images of lost and recovered items provided by owners with photos taken when items are registered as lost and found. A photo matching network is proposed, integrating the transfer learning capabilities of MobileNetV2 with the Convolutional Block Attention Module (CBAM) and utilizing perceptual hashing algorithms for their simplicity and speed. An Internet framework based on the Spring Boot system supports the development of an online lost and found image identification system. The implementation achieves a testing accuracy of 96.8%, utilizing only 0.67 GFLOPs and 3.5M training parameters, thus enabling the recognition of images in real-world scenarios and operable on standard laptops.</p></div>2024-10-30T17:26:21ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0310998.g008https://figshare.com/articles/figure/Confusion_matrix_of_the_model_/27345133CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/273451332024-10-30T17:26:21Z |
| spellingShingle | Confusion matrix of the model. Meihua Zhou (12331454) Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified transfer learning capabilities services like find rapid population growth photo matching network internet framework based 8 %, utilizing 5m training parameters recovered items provided find </ p xlink "> world scenarios urban areas unclaimed items thus enabling testing accuracy smart way search process research presents public transportation public spaces photos taken increased frequency implementation achieves 67 gflops |
| status_str | publishedVersion |
| title | Confusion matrix of the model. |
| title_full | Confusion matrix of the model. |
| title_fullStr | Confusion matrix of the model. |
| title_full_unstemmed | Confusion matrix of the model. |
| title_short | Confusion matrix of the model. |
| title_sort | Confusion matrix of the model. |
| topic | Medicine Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified transfer learning capabilities services like find rapid population growth photo matching network internet framework based 8 %, utilizing 5m training parameters recovered items provided find </ p xlink "> world scenarios urban areas unclaimed items thus enabling testing accuracy smart way search process research presents public transportation public spaces photos taken increased frequency implementation achieves 67 gflops |