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Summary Crack detection provides valuable information concerning the location, extent, type, and severity for structural health monitoring of civil infrastructures. Due to the influence of uneven lighting and imaging noise, as well as the debris, various textures, and materials of the different civil structure, detecting cracks in the surface images of civil infrastructures remains challenging in that there are many false positives and defragmentation of the crack curve. In this paper, an information‐based crack detection method is developed to robustly characterize and detect cracks on a curve‐by‐curve basis. Crack information gain (CIG) is defined to characterize the information of a local patch's being cracked. With the proposed model of estimating crack depth, the information‐based crack descriptor CIG is calculated by statistically modeling the probabilistic distribution of crack depth. Secondly, crack curves of different saliency are progressively detected by iteratively searching the salient string of pixels, regardless of the small gaps between fragments of the crack curve. Finally, crack curves are validated by examining the variation of CIG along the crack curve. The experimental results on a diverse set of images of different civil infrastructures demonstrate the generalizability of the proposed method, and the overall performance on each dataset outperforms the state‐of‐the‐art available methods with 2.9% improvement. The proposed method has potential for the quantitative evaluation of cracks on a meaningful curve basis.

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