Pavement Disease Detection Based On Multi-scale Image Analysis Research And Analysis

Nowadays, the automatic pavement distress detection system based on CCD camera has been developed greatly in the road maintenance project. And the automatic distress detection module will directly affect the measurement accuracy of the whole system, wrong detected and missed will both affect the assessment of the entire road conditions, so designing accurate and effective automatic detection algorithm is a crucial link. Most of traditional auto-detection algorithms analyze the road image in a fixed scale, such methods can not show inherent multi-scale characteristics of images, so the accuracy of analysis results is limited. In this paper, the multi-scale image analysis method which simulates human visual perception of outside things is used to analyze pavement surface images, and around the core we study three areas: pavement surface image denoising, enhancement and crack targets extracting.To aim at the characteristics of pavement surface's rich texture and much background noise, this paper firstly starts from the mathematical morphology scale space, constructs a multi-scale morphological filter, and selects the appropriate morphological computing type, structure element as well as appropriate scale according to the characteristics of road images. And appropriate weight distribution is used according to different anti-noise performance in different scales. This method can be very good at filtering out background noise while maintaining the edge of the target.Then this paper starts from the nonlinear scale space, to aim at the shortcomings of the Gaussian pre-smoothing in the P-M regularization model will bring about the edge position drifting, we modify the diffusion coefficient of the diffusion equation, bring morphological operators to the diffusion equation, then a new P-M model based on morphological operators is formed, the gradient threshold K in the model is given adaptively by a robust statistics method. New diffusion model can remove noise effectively while have better edge retention capacity, and can inhibit the drifting of border.To the situation of small crack information with low contrast to background, filtering is not enough, so in this paper image enhancement technology based on nonlinear scale space is applied to road surface images, and to aim at the linear texture structure of cracks, this paper adopts a coherence-enhancing shock filter which combines structure tensor with the Osher-Rudin shock filter model. This model is not only able to effectively sharpen target's edge but also enhance the contrast of the target and background and, more importantly, enhance the linear texture structure of the cracks. About the extraction of crack information, this paper design a crack detecting method based on multi-scale morphological gradient to aim at the images with uneven background illumination, it avoids wrong segmentation phenomenon caused by uneven overall gray by detecting the local mutations information. This method combines large scale gradient information with small scale's, it overcomes the problem of conventional method's susceptible to noise, debris and lighting conditions, and has achieved good detecting results of cracks in different road conditions.