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pp. 5439-5446
S&M4255 Research Paper https://doi.org/10.18494/SAM5700 Published: December 19, 2025 Sensor-integrated Machine Vision System for Enhanced Defect Detection in Industrial Applications [PDF] Hsin-Chin Chen, Shih-Hung Lin, Ben-Yi Liau, Peter Yang, Kai-Huang Chen, and Yao-Chin Wang (Received April 8, 2025; Accepted November 28, 2025) Keywords: machine vision, defect detection, optical inspection
With rapid advancements in high-tech industries, the demand for precision and efficiency in manufacturing has increased significantly. Automated optical inspection (AOI) systems have become essential across various sectors, expanding from electronics to food and pharmaceuticals, as quality control standards and consumer expectations rise. We present a machine vision approach for defect detection in paper cups, specifically targeting quality issues caused by stains or blemishes. Traditional binary detection methods, such as blob analysis, often struggle with light stain detection. Here, we implement an optical inspection method using blemish edge detection, which more accurately identifies subtle defects along the inner edges of paper cups. By enhancing defect detection accuracy, this method is aimed at improving product quality and optimizing production yield, making it a practical solution for large-scale, high-speed inspection requirements in modern manufacturing.
Corresponding author: Yao-Chin Wang![]() ![]() This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Hsin-Chin Chen, Shih-Hung Lin, Ben-Yi Liau, Peter Yang, Kai-Huang Chen, and Yao-Chin Wang, Sensor-integrated Machine Vision System for Enhanced Defect Detection in Industrial Applications, Sens. Mater., Vol. 37, No. 12, 2025, p. 5439-5446. |