Cargando…
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for...
Autores principales: | Parico, Addie Ira Borja, Ahamed, Tofael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309787/ https://www.ncbi.nlm.nih.gov/pubmed/34300543 http://dx.doi.org/10.3390/s21144803 |
Ejemplares similares
-
An Improved Pig Counting Algorithm Based on YOLOv5 and DeepSORT Model
por: Huang, Yigui, et al.
Publicado: (2023) -
Pear Recognition in an Orchard from 3D Stereo Camera Datasets to Develop a Fruit Picking Mechanism Using Mask R-CNN
por: Pan, Siyu, et al.
Publicado: (2022) -
An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting
por: Lawal, Olarewaju Mubashiru, et al.
Publicado: (2023) -
Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications
por: Durve, Mihir, et al.
Publicado: (2023) -
Research on the Method of Counting Wheat Ears via Video Based on Improved YOLOv7 and DeepSort
por: Wu, Tianle, et al.
Publicado: (2023)