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Related Experiment Video

Updated: May 2, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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HubNet: An E2E Model for Wheel Hub Text Detection and Recognition Using Global and Local Features.

Yue Zeng¹, Cai Meng¹

¹Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|October 16, 2024

Summary

This study introduces HubNet, a novel model for automatically detecting and recognizing text on wheel hubs. HubNet enhances accuracy by integrating global and local features for industrial applications.

Keywords:

deep learning text detection text recognition wheel hub text

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Last Updated: May 2, 2026

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Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Wheel hub text recognition is challenging due to text obscurity and orientation variability.
Accurate product information recording is crucial for industrial efficiency.

Purpose of the Study:

To develop an effective model for automatic wheel hub text detection and recognition.
To create a comprehensive dataset for training and evaluating such models.

Main Methods:

Construction of a wheel hub text dataset with 446 images from industrial production lines.
Proposal of HubNet, an end-to-end model featuring a novel feature cross-fusion module.
Utilizing both global and local features for improved text recognition accuracy.

Main Results:

HubNet achieved 86.5% accuracy, 79.4% recall, and 0.828 F1-score on the custom dataset.
The feature cross-fusion module improved accuracy by 2% to 4%.

Conclusions:

The developed HubNet model and dataset provide a valuable reference for automatic wheel hub text processing.
The proposed methods significantly enhance the efficiency and accuracy of recording wheel hub information.