Machine vision in manufacturing SMEs: a review

Automating manufacturing tasks, such as quality control, fault detection, part classification, and inventory management with machine vision systems can significantly improve process efficiency, accuracy, and productivity. As a result, the machine vision technology market is expanding, largely driven by its applications in manufacturing across both hardware and software sectors. Nevertheless, small- and medium-sized enterprises (SMEs) face distinct challenges in the implementation of such systems due to their human, technical, and organizational constraints. An overview of the current state of research and practical insights is essential to address these constraints and guide future developments. Although some surveys and interviews have been conducted, no comprehensive review outlines scientific literature on research methods and initiatives related to the characteristics and challenges of adopting machine vision systems in industrial SMEs. Therefore, we present a systematic literature review to identify applications, challenges and proposed approaches for machine vision and its adoption in industrial SMEs, analyzing 770 articles. The review highlights quality control as the prominent application, while primary challenges for SMEs include limited investment capacity, labor and expertise shortages, and high-variety, low-volume production, which often leads to insufficient data for training algorithms. Furthermore, the review identifies approaches involving low-cost hardware, open-source software, and intuitive-to-use systems as potential solutions to these challenges. Although many articles contribute to highly specific problems of SMEs, we identified a lack of broader applicable interdisciplinary approaches to integrate machine vision. This article outlines challenges and initiatives for adopting machine vision across different applications to enhance value generation for industrial SMEs facing specific challenges. Future research can leverage our findings to develop industrial solutions or explore new research directions in this domain.