Enhanced Surface Defect Detection in Industrial Manufacturing Using Convolutional Neural Networks and Advanced Imaging Techniques

Abstract

This paper explores advanced methods and techniques for defect detection, focusing on their effectiveness, challenges, and implications for industrial applications. We explore the combination of CNNs with deflectometry and dark-field polarization imaging for surface defect detection in refrigerator manufacturing and optical components inspection, respectively. We highlight the importance of automated inspection systems in detecting surface defects and discuss the challenges associated with real-time defect detection and limited datasets. This study contributes to advancing defect detection methodologies and provides valuable insights for industrial quality control processes.

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DOI

10.4028/p-ksfub8

Citations and references