Linear Seam Elimination of Tunnel Crack Images Based on Statistical Specific Pixels Ratio and Adaptive Fragmented Segmentation

Abstract

Image processing basis crack detection methods can overcome the deficiency of traditional manual and instrument basis crack detection methods, and can provide an important guiding for damage evaluation. However, for tunnel lining surface, cracks and linear seams have great similarities in both intensity value and texture features, it is difficult for existing crack extraction methods to obtain accurate crack segmentation results. In this paper, we proposed a novel algorithm to adaptively eliminate linear seams in tunnel lining crack images. By analyzing characteristics of linear seams and cracks, the idea of binning is used to classify those detected line edges with disordered directions into multiple angle subintervals. Then, by calculating the ratio of statistical specific pixels (SSP ratio) on the expanded line edge, the length and binning information are used to select linear seam edges with the use of adaptive expansion algorithm. Finally, the fragmented segmentations of linear seams are adopted so that cracks and linear seams can be clearly separated, and linear seams can be removed. The experimental results demonstrate the superior precision and efficiency of our method compared with existing methods.