A crack extraction method combining improved MASK dodging and K-means clustering algorithms is proposed to address the problem of stains, shadows and uneven illumination in the captured bridge crack images, which makes it difficult to extract crack features at a later stage. The method firstly improves the MASK dodging algorithm, improves the adaptive capability of the algorithm, uses contrast stretching to enhance the image contrast, then uses the K-means clustering algorithm to segment the image according to the difference between the grey value of cracks and background pixels, and finally combines morphological methods and connected domain detection to bridge and denoise the cracks. The experimental results show that, compared with other methods, this method can effectively reduce the influence of image brightness uneven interference on the crack extraction results, and the crack extraction accuracy reaches 95%, ensuring the accuracy of later crack size measurement and bridge damage degree assessment.