Computer vision in Quality 4.0: empirical insights from industrial demands

Paper aims This paper examines how computer vision (CV) technologies are being integrated into the Quality 4.0 framework, addressing the gap between conceptual discussions and industrial implementation. Originality Unlike previous studies based on simulated environments or theoretical frameworks, this research draws on 202 proofs of concept (POCs) developed with real industrial demands. The dataset provides a rare empirical perspective on how CV technologies are adopted. Research method The study employs a mixed-method approach combining descriptive statistics, trend identification, and correspondence analysis to identify technical and contextual patterns across the POCs. Main findings The analysis reveals challenges in CV adoption, including the need for customization to sector-specific requirements and environmental characteristics. Simultaneously, it identifies opportunities in automated inspection, predictive maintenance, and real-time decision-making. The increasing use of classification, segmentation, and object detection techniques indicates a progression toward greater technical maturity. Implications for theory and practice The findings extend Quality 4.0 research by providing empirical evidence of the technological and organizational conditions shaping CV implementation. For practitioners, they offer actionable insights on aligning CV deployment with infrastructure and production constraints. Overall, this study provides the first large-scale empirical mapping of CV implementation, demonstrating its role as an enabler of data-driven quality management.