Automatic Pavement Distress Detection Based On Image Analysis

Traditional manual methods for pavement distress detection have too manyshortcomings such as time-consuming,dangerous, costly and so on, so it can't meet theneed of pavement's development now. Nowadays, pavement distress detection based ondigital image analysis has been developed greatly. However, the algortims for automaticare still not satisfying. This paper is devoted to reseach the automatic detection algortims,including pavement surface image enhancement,shadow removal,crack imformationabstract,carck classification,crack measurement and crack positioning.In pavement surface images, the noises often very serious and cracks are very tiny, sotwo methods on Partial Differential Equation (PDE) are proposed to enhance thepavement surface. Firstly, a gradient-based coherence enhancing diffusion that enhancesthe cracks and also eliminates the other unwanted noises is proposed. The new diffusiondistinguishes cracks from unwanted elements by gradients on the assumption that thegradients of cracks are approximately unchanged, and takes different strategies to controlcracks and other elements. The new approach also absorbs the idea of forward-backwarddiffusion to determine the strengths and directions of the diffusion process in order tosharpen the edges of cracks. As a result, both the edges and the flow-like structure ofcracks are enhanced. Secondly, a new model fused by P-M diffusion,Shock filter,coherence enhancing diffusion is proposed. On the assumption that images are not noised,3 weight functions depending on the local gradients and the degree of consistency oflocal directional structure are designed, and the 3 basic PDE models are fused togetherby the 3 weight functions. Then according to the characteristics of road surface images,the new model for not-noised images are generalized to process complex road surfaceimages by improving the basic PDE models and the weight functions.The illumination of the pavement surface image often imbalance, even there aresometimes shadows in road surface images and make it difficult to process the image.Images without shadows are processed by a illumination correction method based on thesimulation of the background of the image. As for the shadows, An Anisotropic DiffusionCenter/Surround Retinex (ADCSR) is presented to eliminate it. First anisotropic diffusionbased on PDE is introduced to ADCSR, further a new anisotropic diffusion scheme basedon "Edge Degree"(ED) is presented, which avoids the embarrassment to select different parameters such as gradient threshold. Experimental results show that shadows areeliminated successfully by ADCSR.The contrast of the pavement surface images are enhanced by fuzzy imageenhancement algorithms. Traditional fuzzy image enhancement algorithms can't enhanceimages with changeful grey levels well, also it is difficult to decide the controlparameters, so a new fuzzy image enhancement algorithm is proposed to overcome thedrawbacks. First the crossover points for each pixel are computed adaptively based onthe local feature of the neighborhood of each pixel. Then a new fuzzy membershipfunction is proposed. The new fuzzy membership function is S-shape, and can combinedwith the crossover points perfectly by adjust the parameters. Road surface images withchangeful grey levels can obtain satisfactory enhancement effect by the new algorithm.Also the new algorithm is universal because all the parameters are computed adaptively.New algorithms for detecting and classifying,measuring road surface cracks areresearched systemically. First the 2D pavement surface images are mapped to 3D spatialsurfaces and the cracks that are difficult to describe in 2D images can be regarded as"valleys" in them. Then the "valleys" are detected by differential geometry operator andtaken as cracks in the 2D images. Further the line feature of the real cracks is analyzed,and the lengths of the cracks are obtained by path growing in the consistent direction,cracks that are not long enough are considered to be fake and eliminated. Than a patternclassifier based on BP neural network is designed to recognize different cracks accordingto the geometrical shape difference of different cracks, effective methods for measuringcracks are proposed after that.At last, a fuzzy adaptive federated Kalman filter (FAFKF) is presented for positioningcracks. First, a real-time fuzzy adaptive filter controller is used to monitor the fact valueand theoretic value of residual covariance, and adjust the covariance matrices ofobservation noises towards the real model by enhancing their consistencies. As a result, theKalman filter's tolerance to model error is improved. Then a fuzzy adaptive data fusioncontroller is used to evaluate the reliability of each subsystem, and the informationdistribution coefficients of each subsystem are computed according to the reliability.Theoretical analysis and experimental data show that the precision and fault tolerance ofFAFKF are improved both.