Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures.

Du, Kaixuan; Che, Xianghong; Wang, Yong; Liu, Jiping; Luo, An; Ma, Ruiyuan; Xu, Shenghua

Du, Kaixuan; Che, Xianghong; Wang, Yong; Liu, Jiping; Luo, An; Ma, Ruiyuan; Xu, Shenghua.

Afiliação

Du K; School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China.
Che X; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.
Wang Y; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.
Liu J; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.
Luo A; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.
Ma R; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.
Xu S; Research Center of Geospatial Big Data Application, Chinese Academy of Surveying and Mapping, Beijing 100830, China.

Sensors (Basel) ; 22(19)2022 Oct 07.

Article em En | MEDLINE | ID: mdl-36236693

Assuntos

Inteligência; Taiwan

Palavras-chave

RetinaNet; administrative regions; map pictures; multi-target model; single-target cascading model

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Tipo de estudo: Diagnostic_studies / Prognostic_studies País/Região como assunto: Asia Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article

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