Abstract
Industrial inspection is a critical element in automated quality control, ensuring products meet stringent standards while minimizing human error and operational costs. Machine vision systems have emerged as indispensable tools in modern industrial environments, owing to their ability to perform high-speed and high-precision inspections. Central to these systems is signal processing, which transforms raw image data into actionable insights. This paper explores the core signal processing techniques used in machine vision for industrial inspection, including preprocessing, feature extraction, and defect classification. It also discusses recent advancements in deep learning-based processing and real-time system integration. The synergy of robust signal algorithms and intelligent vision systems has significantly enhanced detection accuracy, manufacturing efficiency, and scalability across industries such as electronics, automotive, pharmaceuticals, and textiles.
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