Intelligent identification and quantification method and system for high-resolution image of slope crack

Abstract

The application discloses a slope crack high-resolution image intelligent identification and quantification method and system, and relates to the technical field of image processing and computer vision. The method comprises the following steps: S1, performing multi-scale light balance processing and multi-scale Gaussian difference filtering processing based on the Retinex theory on the obtained slope high-resolution image in sequence to compensate for non-uniform illumination and suppress background noise; S2, using an improved Canny edge detection algorithm to extract the initial edge from the preprocessed image. The application performs multi-scale light balance and multi-scale Gaussian difference filtering based on the Retinex theory on the slope high-resolution image, effectively compensates for non-uniform illumination and suppresses background noise, solves the problems of light distortion and noise interference in the field scene, and uses the improved Canny edge detection algorithm to combine the gray gradient, direction consistency and texture continuity features to accurately mark the effective crack segments and avoid false crack misjudgment.

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