Research On Key Technologies Of Pavement Crack Inspection Based On Structure Light

With the rapid development of highway construction, pavement inspection andmaintenance management have become an important task for China highwayconstruction. Cracks are one of the most important parameters to evaluate thesurface quality and the early manifestations of most disease, which will affect thelifespan of the pavement and traffic safety directly. It can avoid serious problemscaused by the crack further development if we detect cracks early and maintainthem in time. The major issue with pure2D detection technique is that they can notdiscriminate dark areas not caused by pavement distress such as recent fillings, oilspills, tire marks, shadows and uneven poor illumination. Three-dimensionalpavement crack detection technique based on structured light has the advantages ofhigh accuracy, rich feature and is robust to the shadows, black spots and randomnoise, which can effectively improve the recognition rate of pavement surfacecracks. So this method becomes the new development direction in the field ofpavement crack automatic inspection.The main purpose of this project is to study key issues in the processingtechniques of pavement surface crack data based on structured light and focus onthree key technologies of3D crack detection system based on the structured light,including unable to balance speed and accuracy of three-dimensional data detection,pavement profile signal feature extraction and decision reliabillity problem of crackdetection system. Cracks are reflected in the small deformation of the light stripeduring structured light detection, the extraction of light stripe center must havehigh-precision resistance in order to ensure accurate extraction of the smalldeformation information, in the same time in order to ensure the accuracy of thecracks and other disease characteristics in the three-dimensional data extracted.While the surface data of the pavement is massive, to speed up light stripe centerextraction is also essential. After the light stripe extraction and calibration, thethree-dimensional pavement profile signals have rich disease characteristics. Onlyeach disease has been separated from the main profile well and the information ofmain profile has not been damaged, we can ensure the accuracy of the calculation ofother parameters such as cracks and IRI. Good separation of the disease and themain profile has been the difficulty in the area of profile signal processing. In thedetection process of cracks, inevitably there will be cracks undetected and amiscarriage of justice, analysis of the reliability of crack detection is an importantresearch in crack detection area. This study focus on the problem of Light stripecenter extraction method can not meet the high-precision and high-speed signalcharacteristics, three-dimensional pavement profile more difficult separation problems, the reliability of crack detection system decision-making.The main research works in this paper are as follows:Accuracy and speed of structured light center extraction impact on the dataprocessing accuracy and speed of three-dimensional detection system. Consideringthat structured light center extraction method can not meet high precision, highspeed and pavement image is complex and difficult to extract laser stripe center. Wepropose the laser stripe center extraction method based on ridge-tracing withHessian matrix. This method possess the Hessian’s merits of higher extractionaccuracy, better performance of resistance to noise and less image points usingridge-tracing. Utilize Radon transform to get ROI of laser stripe and trace ridge lineof laser stripe in ROI. In ROI area, calculate the Hessian matrix in a certainneighborhood along the normal direction to find some points satisfying the ridgepoint conditions as the center point of laser stripe. The experiment demonstratesthat our method has better performance in precision, speed and resistance of thenoise.For the problems of cracks are difficult to extract accurately and to beseparated from main profile, we propose the feature extraction method of thepavement cracks, so as to achieve crack precise detection and the separation frommain profile. First, it introduces sparse decomposition theory applied to the field ofpavement profile signal processing and builds over-complete atoms dictionary inaccordance with characteristics of pavement cracks signal.Then signal is separatedby learning in this mixed dictionary with a matching pursuit (MP) algorithm.Experiments show that this method can separate crack characterizes informationand accurately detect the distress parameters without losing the main outline.For crack decision-making reliability problems of structured light3D crackdetection system, a decision model for the laser scanning pavement crack detectionsystem based on the hypothesis test is proposed. First the factors contributed tothese errors in laser scanning system are firstly analyzed, and then a decision modelfor the laser scanning pavement crack detection system based on the hypothesis testis proposed. This model build the relationship between the contribution factors andcrack detection accuracy and can provide guidance on the pavement crack detectionand has practical value. Simulation and the actual road experiment verify theaccuracy and validity of the model.