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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Unmanned forklift pallet positioning algorithm based on an improved human pose estimation model.

Zhuguo Zhou¹, Yujun Lu¹, Liye Lv¹

¹School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, China.

Annals of the New York Academy of Sciences

|August 6, 2025

Summary

This summary is machine-generated.

This study introduces a high-precision pallet positioning system using an improved YOLOv11s-pose model. The method enhances accuracy and efficiency in complex logistics and automation scenarios.

Keywords:

EPnP algorithm YOLOv11s‐pose key point detection pallet positioning pose estimation topological constraints

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Area of Science:

Computer Vision
Robotics
Machine Learning

Background:

Pallet positioning in industrial automation and logistics is hindered by challenges in accuracy and efficiency, particularly with stacking and occlusion.
Existing methods often struggle with complex environments, necessitating advanced solutions for precise pose estimation.

Purpose of the Study:

To develop a high-precision pallet positioning method that overcomes accuracy and efficiency limitations in complex scenarios.
To enhance the detection and localization accuracy of pallet key points and accurately compute the pallet's pose.

Main Methods:

An improved YOLOv11s-pose architecture was utilized, incorporating transfer learning and optimized star operations.
A dual-domain edge feature enhancement module and a region-focused topology attention mechanism within the C3k2 module were employed for multiscale feature extraction and fusion.
An efficient perspective-n-point algorithm, combining visual weights and topological constraint optimization, was used for pose computation.

Main Results:

The server-side model achieved 95.1% object detection accuracy, 94.2% key point localization accuracy, and 105.6 FPS.
On the RK3568 development board, the model maintained detection accuracy with 44.1 FPS, inclination angle error < 2.9°, and horizontal error < 19 mm.

Conclusions:

The proposed method significantly enhances the accuracy and stability of pallet positioning in complex environments.
The integration of pose estimation with transfer learning demonstrates strong potential for industrial automation and logistics applications.