Journal ArticleOpen Access
Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes
Author Affiliations
Western Illinois University, American International University-Bangladesh, University of Aizu
Published InIEEE Access
Year2024
Citations98
Abstract
Ensuring product quality and integrity is paramount in the rapidly evolving landscape of industrial manufacturing. Although effective to a certain degree, traditional quality control methods often fail to meet the demands for efficiency, accuracy, and adaptability in today’s fast-paced production environments. The advent of Deep Learning (DL) and Computer Vision (CV) technologies has opened new vistas for automated defect detection, promising to revolutionize the way industries approach quality control and inspection. This systematic review focuses on recent advancements in DL and CV applications for automated defect detection in manufacturing processes. It provides a comprehensive overview of state-of-the-art techniques for detecting, classifying, and predicting defects, highlighting the significant strides made in addressing challenges such as varying lighting conditions, complex defect patterns,…
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