Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform

• Mobile Mapping Systems using RGB camera for asphalt pavement distress imaging.
• Angular-geometric orientation for classification of linear distresses in asphalt road pavements.
• Linear crack detection, extraction and classification.
• Triple-transform algorithm: discrete wavelet transform, successive morphology transform and circular Radon transform.
• Similarity coefficient measures for crack detection and extraction accuracy assessment.

The combined detection, extraction and identification of incipient or micro-linear distresses in asphalt pavements are important steps in the quantification and analyses of the occurrences of linear distresses for early pavement management and repair (M&R). This study presents an empirical approach for the formalized identification of incipient linear structural failures in asphalt pavements, which are characterized by longitudinal, transverse, diagonal, block (random) and alligator (fatigue) distresses. Because of the spectral and spatial complexities in detecting distress features at very high resolutions, this study presents a triple-transform approach for distress detection, isolation and classification that comprises of: (i) 2D discrete wavelet transform (DWT) for multidirectional and multiscale linear distress detection; (ii) successive morphologic transformation filtering (SMF) as an adaptive filter for the extraction of linear distress shape and continuity, and (iii) circular Radon Transform (CRT) for angular-geometric orientation analysis for the identification and classification of the distress types. Using mobile RGB camera imaging, 72 pavement distress images, at a spatial resolution of about 1 mm were selected for evaluating the proposed approach. The results of the DWT-SMF were validated using the Dice coefficient of similarity between the manually segmented distresses and the study results. The validation results show that the linear distresses are satisfactorily extracted with an average detection rate of 83.2%. The average processing time for implementing the DWT-SMF phase of the algorithm was approximately 125 s. To validate the classifications of the distress types, the CRT results were matched with the reference classifications from synthetic cracks, with all showing positively corresponding results. In overall, the results of the study illustrate that the proposed triple-transform approach provides a reliable approach for the detection, isolation and characterization of linear distresses in flexible asphalt pavements.

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