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Título
Simplifying YOLOv5 for deployment in a real crop monitoring setting
Autor
Año del Documento
2023
Editorial
Springer
Descripción
Producción Científica
Documento Fuente
Multimedia Tools and Applications, 2023.
Résumé
Deep learning-based object detection models have become a preferred choice for crop
detection tasks in crop monitoring activities due to their high accuracy and generalization
capabilities. However, their high computational demand and large memory footprint pose a
challenge for use on mobile embedded devices deployed in crop monitoring settings. Vari-
ous approaches have been taken to minimize the computational cost and reduce the size of
object detection models such as channel and layer pruning, detection head searching, back-
bone optimization, etc. In this work, we approached computational lightening, model com-
pression, and speed improvement by discarding one or more of the three detection scales
of the YOLOv5 object detection model. Thus, we derived up to five separate fast and light
models, each with only one or two detection scales. To evaluate the new models for a real
crop monitoring use case, the models were deployed on NVIDIA Jetson nano and NVIDIA
Jetson Orin devices. The new models achieved up to 21.4% reduction in giga floating-point
operations per second (GFLOPS), 31.9% reduction in number of parameters, 30.8% reduc-
tion in model size, 28.1% increase in inference speed, with only a small average accuracy
drop of 3.6%. These new models are suitable for crop detection tasks since the crops are
usually of similar sizes due to the high likelihood of being in the same growth stage, thus,
making it sufficient to detect the crops with just one or two detection scales.
Materias Unesco
33 Ciencias Tecnológicas
Palabras Clave
Object detection
Model simplification
Crop monitoring
YOLOv5
Deep learning
ISSN
1380-7501
Revisión por pares
SI
Patrocinador
Tertiary Education Trust Fund - TETFUND NRF 2020 with grant number TETF/ES/DR&D-CE/NRF2020/SETI/88/ VOL.1
Agencia Estatal de Investigación (grant PID2020-115339RB-I00 and CPP2021-008880)
European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no.101008297
Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE
Agencia Estatal de Investigación (grant PID2020-115339RB-I00 and CPP2021-008880)
European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no.101008297
Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE
Patrocinador
info:eu-repo/grantAgreement/EC/H2020/101008297
Version del Editor
Propietario de los Derechos
© 2023 The Author(s)
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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