Study On Crack Detection Of Straddle Monorail Traffic PC Beam Surface Based On Image Analysis

With the high-speed development of chinese economy and fast expansion of urban, city rail transit system has been the main trend of city traffic mode in China, but its safety is a key problem to the whole transit system. The PC beam is an important component of Straddle Monorail traffic system, the surface of which has been covered with cracks and other flaws, it causes deficiencies to the safety of system. At present, the inspection is made by manual, which exists low efficiency, cost manpower, high cost, and so on. So the crack detection of Straddle Monorail traffic beam surface should be researched. Design efficient equipment and automatic analysis algorithms are the focus and difficulty problems. The paper proposes a crack inspection system for automatic component detection, including inspection equipment and detection methods such as image denoising, enhancement, crack segmentation, flaw classification, flaw measurement, and so on.First, the paper proposes a crack and flaw inspection frame of PC track beam, including data acquisition system and off-line processing system. The data acquisition system consists of line camera selection, data acquisition and save, lighting, precise locating, and other hardware integration problems which should be pain attention, and the hardware platform is built for track beam data collection. The off-line processing system briefly introduces the work flow of different flaw inspection module and the crack detection strategies of track beam base on image analysis.For the problems of track beam image polluted by noise, crack edge blurred and low signal to noise, a new kernel anisotropic diffusion noise removal algorithm is proposed in this paper. On the basis of anisotropic diffusion, an enhance operator which promotes the weak crack edge is added, and according to the characteristics of noise uniformly distributed in the multidimensional space, the low dimensional data is promoted to high dimensional space and denoised in the kernel space, also average absolute difference value of automatic diffusion termination criterion is introduced to enhance the efficiency of diffusion. This method has been applied to noise removal of low signal-noise ratio track beam surface crack, the kernel anisotropic diffusion outperforms other method for denoised result and signal-noise ratio, it thus provide the bases for the behind successful crack detection and precise positioning.The crack is difficult direct detection for it is usually weak, and has low contrast as comparison with background. Then, a new algorithm of crack enhancement of straddle-type monorail track beam surface based on non-subsampled contourlet transformation (NSCT) is presented. It is according to the characteristics of NSCT different domain, the fractional differentiation can enhance the mid-frequency component and retain the low frequency component nonlinearly, then the smooth sub-band texture of NSCT domain is enhanced by the fractional differentiation; at the same time, according to the direction sensitivity of high-frequency sub-band of NSCT domain, the weak edge is enhanced nonlinearly, strong edge is retained, and noise is removed. Experimental results show that the method proposed in this paper has greatly improved visual effects and contrast improve index, and the enhancement effect is good.Two methods for crack extracting have been proposed in this paper. The first one is automatic threshold segmentation approach based on convex residual. The proportion of crack is zero or small comparing to the background, so the histogram distribution is unimodal or close to unimodal. The histogram is regarded as an area of plane, according to the convexity and concavity of histogram of detected image, first compute the convex hull function, then obtain the convex residual which is the difference between convex hull function and the corresponding probability. The maximal value of convex residual join with between-class variance is the threshold value. Another one is based on the curvature of crack. The two dimensional images are mapping to three dimensional surface, starting from the differential geometry, the three dimensional surface curvature characteristics is computed, and the crack is extracted when the gaussian curvature is achieved extreme and the average curvature is negative. There are pseudo cracks appeared in the results, then area, rotundity degree, ratio of length and width are combined to judge between them, they can eliminate defects which are too small or too short, then most of pseudo cracks can be successfully removed.According to the different geometry type of cracks, the ratio of x axis projection and y axis projection of crack, crack area, and angle variance are selected to identify different crack type, and neural network classifier is designed to achieve precise classification of cracks. As well as, for the measure of crack and flaw, crack area, single crack length and width are adopt to measure, and the result error is acceptable, which verifies the validity of the algorithm.