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Machine vision-based detection method for buzzer iron cores
Xiaoyang Liu1, Chenxin Sun1, Cheng Wang1
1Faculty of Automation, Huai'an University, Huaian, Jiangsu province, China.
Plos One
|August 11, 2026
Summary
This study introduces a machine vision method for automatically detecting buzzer iron cores. The system accurately locates, identifies orientation, and measures the notch angle of iron cores, improving automated manufacturing efficiency.
Area of Science:
- Robotics and Automation
- Machine Vision
- Electronic Components Manufacturing
Background:
- Buzzer iron cores are essential acoustic components in electronic systems.
- Current mechanical feeding methods for iron cores limit automation efficiency.
Purpose of the Study:
- To develop a machine vision-based method for automatic detection of buzzer iron cores.
- To enhance the efficiency of automatic feeding and placement of iron cores in manufacturing.
Main Methods:
- Utilizing the Hough transform for iron core localization and exclusion of overlapping cores.
- Employing edge pixel statistics to identify upward-facing cores.
- Calculating notch angle by analyzing the core's circumference for precise localization.
Main Results:
- Achieved a mean relative localization error of 2.61% for iron cores.
- Demonstrated 100% recognition precision for front-up iron cores.
- Obtained an average notch-angle measurement deviation of 1.06°.
Conclusions:
- The proposed machine vision method significantly improves the accuracy and practicality of iron core detection.
- This system provides crucial data for robotic grasping and placement, outperforming YOLOv8 in this application.
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