Threedimensional pavement crack detection based on primary surface profile innovation optimized dualphase computing Abstract   Accurate pavement crack detection has long been a challenging task, causing significant difficulties to the pavement management sectors in the managerial decision making. The high complexity of the crackxe2x80x99s characteristics and the less effective of the crack analytical tools are the two crucial aspects to be accounted for. Recently, threedimensional (3D) technology based high precision crack detection methodologies has undergone extensive developments. Nevertheless, none of those methods has taken into the errors caused by the data collection systems into consideration, resulting in a less satisfying performance. Hence, the primary objective of this research is to outline the Primary Surface Profile (PSP) optimized dualphase computing 3D crack detection methodology. Two years ago, variations caused by the automatic 3D data collection systems were observed, so researchers proposed PSP based data filtering algorithm. Therefore, this research is the upgrade solution of the previous innovation regarding the unbiased 3D pavement crack detection. Firstly, the dualphase computing approach is proposed in dealing with the nonvariance 3D data. Then, the selfadaptive 3D PSP generation method is introduced. Finally, PSP is embedded in the dualphase computing method for performance optimization. For performance assessment, both precisions and recalls of the proposed approach are compared with conventional method for transverse, longitudinal, and map crack detections. Even crack detection precisions are found for both methods, which are all higher than 0.9. However, the recalls of the proposed method (transverse cracks:0.973, longitudinal cracks:0.981, map cracks:0.940) are significantly outperforming nonoptimized dualphase computing method (transverse cracks: 0.682, longitudinal cracks: 0.789, map cracks:0.811).

Pavement Images Denoising with Cracks Detection and Classification Using 2D Discrete Wavelet Transform and SavitzkyGolay Filters Image processing has gained an increased usage and impact in modern pavement networks automatic distress severity classification (DSC). DSC defines priorities and maintenance resources optimum allocation in order to achieve a costeffective rehabilitation process. This paper presents a novel computer vision algorithm having the ability to process, isolate and evaluate the distress severity level of a pavement. A pavement color image is converted to grayscale and then processed for image denoising of the granularity and complex texture that represent and artifact in cracks edge detection. The processing is achieved by a 2D dualtree double density wavelet transform filter banks that significantly reduces the granularity noise while preserving the pavement cracks for edge detection. The 2D wavelet FIR filters perform analysis, soft thresholding then a synthesis of the image. The second step is then an edge detection process followed by morphological filtering and labeled components sizehistogram filter to isolate false edges as residuals of denoising. A final step is performed by two SavitzkyGolay filters for the detection of longitudinal and transverse alligator cracks projections. A weighted score function with multiple parameters is used for DSC.