A Coarse-to-Fine Framework for Oil-Water Interface Measurement in Small-Caliber Transparent Test Tubes

Accurate oil-water interface measurement in small transparent test tubes is important for subsequent volume readout in laboratory analysis. However, manual observation and conventional vision-based methods are easily affected by illumination variation, wall stains, and bubbles, while deep learning detectors alone usually provide only coarse semantic perception. To address this issue, a coarse-to-fine framework is proposed for robust oil-water interface measurement. In the coarse stage, YOLOv8n is used to provide semantic constraints for subsequent processing. In the fine stage, a Fisher-discriminative chromatic-weighted brightness feature is constructed from RGB information, where the RGB weights are derived from the Fisher criterion to enhance oil-water chromatic separability rather than using fixed grayscale or empirical channel weights. This feature is then fused with a SobelY-based vertical-gradient feature to improve interface localization. A stain-aware row-aggregation strategy with effective-pixel compensation is further introduced to suppress artefact interference. The validated interface position is finally converted into a volume readout, with additional correction for bubble-induced bias. The framework was validated on sampled frames from a complete shale-oil core pressing process conducted under mixed-lighting conditions. Stage-wise evaluation and ablation results indicate that the proposed design improves readout stability under stains, bubbles, and illumination variation, achieving a mean absolute error of 0.0159 mL and keeping the maximum error below 0.03 mL in the current experimental setup.