<p>Ensuring dimensional accuracy and structural correctness of metallic spare parts is essential for maintaining product reliability in industrial environments. However, inspection of reflective, non-planar components is challenging due to specular highlights, broken edges, inconsistent gradients, and perspective-induced scale variations across hole positions at different depths, which degrade the performance of conventional circle-detection methods. To address these limitations, this research proposes RCIMI (Robust Circular Identification and Metrological Inspection), a lightweight and interpretable computer-vision framework for classification and dimensional measurement of industrial parts containing circular features. RCIMI integrates a reflection-aware preprocessing strategy to stabilize edges on shiny metallic surfaces, followed by a physically guided Hough parameter stabilization mechanism that constrains radius ranges and accumulator thresholds based on tolerance specifications, improving detection repeatability under varying orientations and illumination conditions. Detected circles are refined through geometric filtering and used for part identification via hole count, followed by a pixel-to-millimetre calibration model that compensates for depth-dependent scale variation arising from non-planar tilted geometry, enabling precise dimensional verification against tolerance limits. Experimental evaluation on real-time industrial data with a mean absolute measurement error of 0.03&#xa0;mm and external images demonstrates a highly stable circle localization, enabling reliable classification of visually similar components. The results confirm that RCIMI provides a practical, training-free solution for automated quality inspection of reflective non-planar components in manufacturing environments where interpretability, metrological traceability, and computational efficiency are critical.</p>