Crack image recognition on fracture mechanics cross valley edge detection by fractional differential with multi-scale analysis

Abstract

The recognition of pavement cracks is crucial in road engineering and airport maintains. In order to successfully apply image processing technique for automatic crack detection, the first and hardest task is to recognize crack images in a huge number of pavement images. To do this, the image processing technique and Fracture mechanics are combined first time in this area, the studied method includes four steps: (1) The pavement crack image shrinking is carried out by a proposed multi-scale analysis algorithm, which is more effective for both preserving weak valley edges and reducing computing cost; (2) Then, a so called valley edge detection algorithm based on Fractional differential for finding local dark line/curve is studied for tracing crack segments, it considers template size, weighted average gray level value in each line in four different directions, the output can be a gradient magnitude image or a binary image; (3) In the binary image, the crack segments are refined based on a number of post processing functions to remove noise and fill segment gaps; and (4) After that, to quickly judge if the image has cracks, Fracture mechanics is applied to calculate the judgment parameter T, which is directly proportion to the image edge density, and the ratio between the average gradient magnitude value and the average gray level value in the candidate crack segment. In experiments, more than 400 pavement images (the resolution is 4096 × 2048 pixels) are tested, and the crack identification accuracy is up to 97%.