Images Crack Detection Technology based on Improved K-means Algorithm

Crack detection is very important to prevent major accident in civil engineering works, but it is still problematic in implementation. The traditional K-means algorithm only takes pixel values into account, which causes the extraction of pavement crack is not accurate. In order to improve the efficiency and accuracy, a novel algorithm is proposed. It is a combination of the improved K-means algorithm and the region growing algorithm, which designs a novel distance function and increases a weight related to crack distance region. The proposed algorithm can effectively abstract the crack information in non-uniform illumination, and improve the performance. The algorithm firstly utilizes histogram algorithm to find the initial clustering center, and then uses the improved K-means algorithm to extract crack. This algorithm overcomes the drawbacks of center indeterminacy and slow speed. Applying the improved K-means algorithm to extract pavement crack image with non-uniform illumination can solve the problem of crack extraction and enhance the reliability and accuracy of pavement crack detection. The results show that compared with traditional K-means algorithm, our proposed algorithm has remarkable effects and can extract the crack information in condition of non-uniform illumination.