A novel automatic dam crack detection algorithm based on local-global clustering

Dam crack detection is necessary to ensure the safety of dams. However, traditional detection methods always perform poorly, with a low detection rate and high false alarm rate, due to the complex underwater environment. In this paper, a novel automatic dam crack detection algorithm (CrackLG) is proposed based on local-global clustering analysis that can find cracks on dam surfaces accurately and quickly using images as well as reduce human subjectivity. First, an image shot of an underwater dam surface is divided into non-overlapping image blocks after pre-processing. Then, image blocks containing crack pixels are identified by local clustering analysis. Second, the image is binarized by adaptive bi-level thresholding based on the local gray intensity. Meanwhile, some noise is removed based on the computed optimal threshold. After extracting global 3-D features, final crack regions are obtained by global clustering analysis. The advantage of CrackLG is that the threshold for realizing image binarization is self-adaptive. Additionally, it can automatically perform crack detection without human supervision. The simulation and comparison show that the proposed CrackLG method is more effective for underwater dam crack detection.