The present application relates to a kind of
railway tunnel disease intelligent detection method, including
mixed resolution image acquisition,
mixed resolution normalization preprocessing, double channel parallel
disease detection,
physical quantity calibration based on dynamic precision coefficient, dynamic splicing
algorithm based on ROI
feature fusion and mileage error dynamic calibration based on semantic
anchor point.The present application is characterized in that, by
mixed resolution normalization preprocessing, the compatibility problem of heterogeneous hardware is solved, the
disease feature extraction precision of key area is retained using double-scale
cascade detection mechanism, with dynamic precision
coefficient matrix, the pixel
physical mapping deviation caused by heterogeneous resolution is corrected, the accuracy of disease quantitative analysis and
physical quantity inverse calculation is ensured;The present application significantly improves the
processing efficiency of ultra-
high resolution image, eliminates the mileage cumulative positioning error in long-distance inspection, realizes the full-quantitative accurate detection of track traffic tunnel disease in low-cost, high-efficiency environment.