An image crack detection method with multiscale downsampled normalized cut is proposed to address the problems of low precision for edge detection and timeconsuming for feature vector solution with the multiscale normalized cut. Firstly, the feature of halfreconstruction of the antisymmetrical biorthogonal wavelet is used to extract the multiscale edge features of the test image Then, combining the strength feature and location feature of each scale, the multiscale similarity matrix and multiscale normalized similarity matrix are obtained. Spectral segmentation method is utilized to calculate the downsampled feature vector of the multiscale similarity matrix after downsampling. Finally, by multiplying the multiscale normalized similarity matrix with the downsampled feature vectors, the segmentation result is obtained after discretization. Experimental results indicate that the proposed method improves the accuracy of detection and reduces the computational time on three image datasets, compared with other methods.