BRIDGE PAVEMENT CRACK DETECTION UNDER UNEVEN ILLUMINATION USING IMPROVED PCNN ALGORITHM
For bridge pavement cracks under uneven illumination, the existing image segmentation algorithm does not remove this effect, and the segmentation effect is affected. In this paper, the image preprocessing consists of two parts: the process of removing uneven illumination and image noise, and the traditional bilateral filtering is improved based on the stationary wavelet algorithm (Cross-bilateral filtering). In the image segmentation part, the traditional PCNN (Pulse Coupled Neural Network) model parameters and the number of iterations are difficult to determine reasonably, and the use of a certain complexity makes it difficult to automate. This paper combined the synaptic integration characteristics of neurons, image gray and spatial features, to simplify PCNN model. The improved PCNN algorithm (SPCNN) based on the gray threshold of Markov network directly completes the segmentation without the need to manually set parameters and determine the optimal number of iterations. Through the analysis of the experimental results, the following three conclusions were drawn. (1) Compared with the histogram equalization, the enhancement algorithm of this paper removed the influence of illumination well and had advantages for the subsequent segmentation processing. (2)The cross-bilateral filtering algorithm could improve the image signal-to- noise ratio from 18.855 to 32.037, which was better than the original bilateral filtering algorithm. (3) The average segmentation accuracy rate of segmentation of SPCNN algorithm was more that 90%. Compared with the traditional PCNN method, this method is better in subjective visual effects and objective segmentation performance, less time consuming.