Research Status and Trends of Industrial Automation Based on Computer Vision Technology

Driven by Industry 4.0 and intelligent manufacturing, computer vision has emerged as a core technology in industrial automation due to its non-contact sensing capabilities and real-time processing advantages. However, challenges such as environmental interference and real-time performance limitations persist. A bibliometric analysis of $\mathbf{1, 0 0 0}$ publications (2014-2025) using VOSviewer reveals several key findings: author productivity is characterized by a stratified distribution, with a limited number of high-impact scholars; high-output research institutions are scarce, and collaborative networks remain underdeveloped; globally, research is led by China and the United States within a multi-polar framework, with increasing international collaboration; and there is a clear transition from traditional image analysis to deep learning, with artificial intelligence serving as a pivotal driver for future innovation. Despite progress, environmental adaptability and real-time efficiency remain critical unresolved issues. Future research should prioritize the enhancement of algorithm robustness, interdisciplinary collaboration, standardization efforts, and greater involvement of the industrial sector.