ReSTiNet: An Efficient Deep Learning Approach to Improve Human Detection Accuracy.

Sumit, Shahriar Shakir; Rambli, Dayang Rohaya Awang; Mirjalili, Seyedali; Miah, M Saef Ullah; Ejaz, Muhammad Mudassir

Sumit, Shahriar Shakir; Rambli, Dayang Rohaya Awang; Mirjalili, Seyedali; Miah, M Saef Ullah; Ejaz, Muhammad Mudassir.

Afiliación

Sumit SS; Department of Computer & Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak 32610, Malaysia.
Rambli DRA; Department of Computer & Information Sciences, Universiti Teknologi PETRONAS (UTP), Seri Iskandar, Perak 32610, Malaysia.
Mirjalili S; Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD 4006, Australia.
Miah MSU; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea.
Ejaz MM; University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.

MethodsX ; 10: 101936, 2023.

Article en En | MEDLINE | ID: mdl-36578294

Palabras clave

Computer vision; Human detection; Low memory devices; Object detection; ReSTiNet; Tiny convolutional neural network

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: MethodsX Año: 2023 Tipo del documento: Article País de afiliación: Malasia Pais de publicación: Países Bajos

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