Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations DOI
Du Q. Huynh,

Huan Thien Tran

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Abstract Pick-and-place robots play a crucial role in industrial automation, helping to lower labor costs, minimize errors, and improve production efficiency. Many image processing methods have been proposed facilitate the pick-and-place operation. However, performance of these is sensitive lighting conditions, presence occlusions, variations object appearance. Although many challenges can be overcome through use deep learning methods, direct comparison coupled with an analysis different picking strategies, lacking. The present study addresses this gap by conducting simulation-based evaluation accuracy time ORB algorithm YOLOv8 model for recognition. effects two strategies (FIFO Euclidean Distance) on system throughput are also explored. simulation results show that achieves higher (98%) significantly faster (138 ms) than (97.33% 715.24 ms time). Additionally, FIFO strategy improves productivity 13% compared Distance strategy. Overall, findings provide valuable insights into optimizing robotic operations automation settings.

Язык: Английский

YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models DOI

Zhichao Meng,

Xiaoqiang Du, Ranjan Sapkota

и другие.

Computers in Industry, Год журнала: 2024, Номер 165, С. 104231 - 104231

Опубликована: Дек. 19, 2024

Язык: Английский

Процитировано

6
A deep learning-based method for silkworm egg counting DOI

Hongkang Shi,

Xiao Chen, Minghui Zhu

и другие.

Journal of Asia-Pacific Entomology, Год журнала: 2025, Номер unknown, С. 102375 - 102375

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
FCAE-YOLOv8n: a target detection method for immature grape clusters DOI

Jidong Lv,

Zhen Wu, Peng Zhou

и другие.

New Zealand Journal of Crop and Horticultural Science, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Янв. 15, 2025

In order to quickly and accurately detect immature grape clusters in the complex environment of orchard, a new model FCAE-YOLOv8n was proposed with low hardware requirements, high target accuracy fast convergence. The improved based on original YOLOv8n model. Firstly, possessed numerous parameters unsuitable for mobile terminal deployment. Faster Block module used replace Bottleneck C2f, which reduced parameters. Secondly, similarly-colored backgrounds, small-sized grains, mutual occlusion among contributed its poor recognition. CA mechanism embedded improve feature extraction ability. Finally, EIoU loss function instead CIoU accelerate convergence bounding interval. experimentally compared different models using self-build datasets. achieved precision 98.6%, recall rate 97.1% [email protected]:0.95 92.5%. It increases by 1.2%, 2.0%, 3.4% respectively YOLOv8n. average detection speed each image increased. rapid precise recognition orchards, provided technical support subsequent automated bagging.

Язык: Английский

Процитировано

0
Simulation-Based Performance Assessment of ORB, YOLOv8, and Picking Strategies for Single-Arm Robot Conveyor Belt Pick-and-Place Operations DOI
Du Q. Huynh,

Huan Thien Tran

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Abstract Pick-and-place robots play a crucial role in industrial automation, helping to lower labor costs, minimize errors, and improve production efficiency. Many image processing methods have been proposed facilitate the pick-and-place operation. However, performance of these is sensitive lighting conditions, presence occlusions, variations object appearance. Although many challenges can be overcome through use deep learning methods, direct comparison coupled with an analysis different picking strategies, lacking. The present study addresses this gap by conducting simulation-based evaluation accuracy time ORB algorithm YOLOv8 model for recognition. effects two strategies (FIFO Euclidean Distance) on system throughput are also explored. simulation results show that achieves higher (98%) significantly faster (138 ms) than (97.33% 715.24 ms time). Additionally, FIFO strategy improves productivity 13% compared Distance strategy. Overall, findings provide valuable insights into optimizing robotic operations automation settings.

Язык: Английский

Процитировано

0