Figures Abstract As a fundamental acoustic component, the buzzer is widely used in various electronic systems. The iron core is a critical element in buzzers for supporting the coil, and it is currently fed primarily by mechanical methods. To further improve the automatic feeding efficiency of iron cores, a machine vision-based detection method is proposed to achieve core localization, pose recognition, and notch-angle measurement. The method first employs the Hough transform to locate iron cores on the vibratory tray and exclude overlapping cores. It then statistically counts the edge pixels around each core's center to select only those facing upward. Finally, by traversing the core's circumference, it pinpoints the notch localization and computes its angle. This providing the necessary data support for the automatic grasping and placement of iron cores by the manipulator. Experimental results demonstrate that the Hough transform algorithm adopted in this paper achieves a mean relative localization error of only 2.61%, a recognition precision of 100% for front-up iron cores, and an average notch-angle measurement deviation of 1.06°. Compared with YOLOv8, the proposed method offers clear advantages in both detection accuracy and practicality. Citation: Liu X, Sun C, Wang C, Huang X, Zhu R, Hu C (2026) Machine vision-based detection method for buzzer iron cores. PLoS One 21(8): e0354351. https://doi.org/10.1371/journal.pone.0354351 Editor: Wislei Riuper Osório, UNICAMP, University of Campinas, BRAZIL Received: April 25, 2026; Accepted: July 7, 2026; Published: August 11, 2026 Copyright: © 2026 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data underlying the results of this study are available from GitHub (https://github.com/obito0330/Leoxy). Funding: This research was funded by National Nature Science Foundation of