YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection.

Lv, Haohui; Yan, Hanbing; Liu, Keyang; Zhou, Zhenwu; Jing, Junjie

Lv, Haohui; Yan, Hanbing; Liu, Keyang; Zhou, Zhenwu; Jing, Junjie.

Afiliação

Lv H; School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Yan H; School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Liu K; School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Zhou Z; School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.
Jing J; School of Automation, Chengdu University of Information Technology, Chengdu 610225, China.

Sensors (Basel) ; 22(15)2022 Aug 07.

Article em En | MEDLINE | ID: mdl-35957461

Assuntos

Pedestres; Algoritmos; Humanos

Palavras-chave

DIoU_NMS; attention module; context extraction module; deep learning; model pruning; pedestrian detection

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pedestres Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Pedestres Idioma: En Ano de publicação: 2022 Tipo de documento: Article