Crack detection based on digital image processing is more and more widely applied in the maintenance of concrete dam diseases. However, due to the complexity of the crack image, it is difficult to achieve high accuracy of the crack classification. To improve the shortcomings and deficiencies in cracks extraction and classification algorithm under crack detection system this article focuses on the application of general data field to effectively solve the problem of crack classification. We propose a new data field clustering method for classification of concrete dam cracks. Clustering is an important step when building a classifier for dam crack. Clustering is a process of discovering densely populated regions. In the data space, it groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. The mutual information (MI) of two grids is a measure of the grid’s mutual dependence. This definition is useful in the field of clustering, because it gives a way to quantify the relevance between different grids. A data field clustering method for classification of dam cracks adopts the potential values within grid and potential values between different grids. Well-known crack classification methods are compared with our method. The experimental results show that the proposed method has an obvious increase on the precision and interpretability.
