The invention relates to a pavement crack
image detection method. The method comprises the steps of: carrying out graying and filtering
processing on a collected pavement image, constructing a pulse
coupling neural network PCNN model, utilizing a
genetic algorithm to rapidly find advantages of an optimal solution in a non-linear manner in a solution space so as to optimize important parameters of the model, and rapidly and accurately segmenting cracks and a background in the image; then according to the characteristics of the image after the segmentation, carrying out connected domain detection on the whole image, and filtering out the interference of
noise and background textures; and finally, extracting a crack skeleton, calculating the maximum widths of the cracks along the normal line of the skeleton, and making marks in the original image. According to the invention, the
digital image processing technology is adopted, the
genetic algorithm is utilized to optimize the parameters of the PCNN model, optimization searching is accelerated, the iteration times f the PCNN are reduced, and the iteration is more liable to come to convergence, the
interference resistance of the segmentation effect is relatively high, and the segmentation is more accurate; in addition, the
modes of connected domain rectangularity, circularity filtering and irregular
noise filtering are utilized to filter out a large number of irregular patches, and convenience is provided for the crack detection.