APPLICATION OF DEEP LEARNING IN IMAGE PROCESSING FOR INDUSTRIAL INSPECTION
DOI:
https://doi.org/10.66238/jaeee4Keywords:
Deep Learning, Image Processing, Industrial Inspection, Defect Detection, Convolutional Neural Networks (CNN)Abstract
Industrial inspection is a critical process for quality control and safety assurance in manufacturing. Traditional inspection methods are often time-consuming and prone to human error. The integration of deep learning (DL) with image processing has revolutionized industrial inspection by enabling automated, accurate, and real-time defect detection and classification. This article explores various deep learning techniques applied to image processing in industrial inspection systems. The study highlights convolutional neural networks (CNNs), transfer learning, and unsupervised learning frameworks for defect detection in metals, electronics, and textile industries. Challenges including dataset availability, real-time processing, and model interpretability are also discussed. Experimental results from recent industrial applications illustrate improved accuracy and efficiency, emphasizing the transformative role of DL in modern industrial inspection.
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