The reliability of optical measurements has become a critical constraint in modern automotive diagnostic systems. While advances in computer vision and sensor resolution have significantly improved perceptual capabilities, measurement accuracy and reproducibility remain highly sensitive to environmental variability when optical systems are deployed outside controlled laboratory conditions. This discrepancy creates a persistent metrological gap between laboratory-grade precision and service-level operation.In this paper, I present an adaptive calibration and photometric normalization framework designed for optical measurement systems operating in non-structured automotive environments. The proposed approach integrates continuous geometric recalibration, context-aware photometric correction, and uncertainty-aware data normalization into a unified adaptive architecture. Particular attention is given to scenarios involving non-standard vehicle geometries and heterogeneous surface materials, where conventional static calibration techniques exhibit pronounced instability.Experimental evaluation demonstrates a substantial improvement in measurement stability and reproducibility under real-world conditions. The proposed framework establishes a robust metrological layer suitable for integration into automotive cyber-physical systems, digital twin architectures, and data-driven diagnostic pipelines.
E. K. Popov (Fri,) studied this question.