The Optical Nondestructive Examination For Pavement Distress

The optical nondestructive examination is involved with many kinds of disciplinedomains, such as optics, image processing, information processing, and pattern recognition.The technology can measure the observation object without interferencing the field. In thisdissertation, the pavement damage automatic detection arithmetic is proposed based on theoptical nondestructive examination. In the current pavement damage detection arithmetics,there exist the defects such as low accuracy, narrow scope of application and lowautomation level. Then we lay stress on the method of abstracting and classifying thecracks under the complicated background. The main work is described as follows:1. The implement algorithm of adaptive fuzzy image enhancement is proposed inRidgelet transform domain. The discrete Radon projection slice theorem is brought basedon the integral projection of Radon transform and original field distribution in the Fourierdomain. The basic condition of reconstructing the original image through Radon transformis proposed. The algorithm of adaptive fuzzy image enhancement is put forward based onthe generalized fuzzy set and the maximum fuzzy entropy. The processed image is thebetter compromise between enhancing the characteristics and inhibiting the noise.2. The pavement image denoise algorithm is proposed based on the Curvelet transform.Curvelet transform generalized the Ridgelet feature representing the straight line and theWavelet trait characterizing the point. And it takes full advantage of the multiscale analysis.It is applicable to enhance the alligator cracks. The method of adaptive denoise is proposedbased on the local information denoted by the Curvelet transform coefficient matrix.Derived from the Curvelet high frequency coefficients of local central point, the processedCurvelet coefficients can enhance the object.3. In order to analyze image singularity and the features of the different sections, a newmultifractal algorithm is proposed based on sub-pixel edge measure. The greylevelgradient areal density function and edge-measure of random subsets (radii can reach theprecision of sub-pixel) are obtained by the square aperture sampling law on the position ofsub-pixel. Utilized the multifractal frame, the image can be segmented into a series fractalsets of the different singularity exponents. The existing cracks can be judged by probabilitydistribution of singularity exponents and the most singular exponent.4. In the end, the automatic detection system for pavement damage is presented. It isconsisted of image collection and pavement damage detection. The working parameters of the apparatus and the workflow of the detection have been presented in details.Now thesystem has been gone to service and completed several highway detection tasks. Theresults show that the accuracy of the indexes of the experiments has reached above 90%.