Application of Machine Vision Techniques for Automated Quality Inspection

Abstract

Machine vision has become an indispensable component of modern industrial automation, enabling accurate, efficient, and consistent quality inspection. Unlike conventional manual inspection, which is prone to fatigue and inconsistency, machine vision offers non-invasive, objective, and high-speed evaluation of products across diverse manufacturing domains. This review examines the evolution, architecture, and contemporary implementation of machine vision techniques for automated inspection tasks. Emphasis is placed on image acquisition methods, illumination strategies, feature extraction, and classification algorithms, including the transition from classical image processing to advanced deep learning–based models. Key challenges such as variable lighting, occlusion, surface reflectivity, and computational cost are analyzed alongside the emergence of real-time inspection enabled by edge AI and optimized convolutional neural networks. The paper also reviews case studies demonstrating applications in  electronics, automotive systems, food processing, textile inspection, and pharmaceutical packaging. The integration of robotics, IoT environments, and cloud analytics further expands the capabilities of machine vision-based inspection systems. The review concludes by considering future innovations such as self- learning inspection pipelines, multimodal imaging, explainable AI, and 3D vision technologies poised to reshape industrial quality assurance.