A Survey on Machine Vision Techniques for Automated Quality Inspection in Industry

Automated quality inspection is a central component of modern industrial production processes. Over the past few decades, machine vision has evolved from rule-based, traditional image processing methods to data-driven machine learning and deep learning approaches. In particular, with the advent of powerful neural networks, significant progress has been made in the detection, classification, and localization of defects. At the same time, industrial applications place high demands on robustness, real-time capability, explainability, and the handling of rare or unknown defect patterns. This brief survey provides an overview of machine vision methods for industrial quality inspection. It systematizes classical image processing approaches, supervised, unsupervised, and semi-supervised learning methods, and discusses their strengths and limitations in real-world production environments. Furthermore, it examines multisensory and three-dimensional inspection approaches, aspects of industrial implementation, and current developments in the field of explainable artificial intelligence. Finally, this brief overview identifies outstanding challenges and research gaps and outlines future trends in automated quality inspection.