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Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method.
Zhao, Haipeng; Zhou, Yang; Zhang, Long; Peng, Yangzhao; Hu, Xiaofei; Peng, Haojie; Cai, Xinyue.
Afiliación
  • Zhao H; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
  • Zhou Y; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
  • Zhang L; Beijing Institute of Remote Sensing Information, Beijing 100192, China.
  • Peng Y; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
  • Hu X; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
  • Peng H; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
  • Cai X; The Institute of Geospatial Information, Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
Sensors (Basel) ; 20(7)2020 Mar 27.
Article en En | MEDLINE | ID: mdl-32230867
Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices. Based on YOLO-LITE as the backbone network, Mixed YOLOv3-LITE supplements residual block (ResBlocks) and parallel high-to-low resolution subnetworks, fully utilizes shallow network characteristics while increasing network depth, and uses a "shallow and narrow" convolution layer to build a detector, thereby achieving an optimal balance between detection precision and speed when used with non-GPU based computers and portable terminal devices. The experimental results obtained in this study reveal that the size of the proposed Mixed YOLOv3-LITE network model is 20.5 MB, which is 91.70%, 38.07%, and 74.25% smaller than YOLOv3, tiny-YOLOv3, and SlimYOLOv3-spp3-50, respectively. The mean average precision (mAP) achieved using the PASCAL VOC 2007 dataset is 48.25%, which is 14.48% higher than that of YOLO-LITE. When the VisDrone 2018-Det dataset is used, the mAP achieved with the Mixed YOLOv3-LITE network model is 28.50%, which is 18.50% and 2.70% higher than tiny-YOLOv3 and SlimYOLOv3-spp3-50, respectively. The results prove that Mixed YOLOv3-LITE can achieve higher efficiency and better performance on mobile terminals and other devices.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza