Work

Sensor-integrated Machine Vision System for Enhanced Defect Detection in Industrial Applications

Year: 2025

Type: article

Abstract: 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.

Source: Sensors and Materials

DOI: 10.18494/sam5700

Authors: Hsin-Chin Chen, Shih-Hung Lin, Ben-Yi Liau, Peter Yang, Kai-Huang Chen, Yao-Chin Wang

Institutions: Cheng Shiu University, National Yunlin University of Science and Technology, Feng Chia University, National University of Tainan

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $748

Funder: Cheng Shiu University