A small amount of research has been conducted in dealing with pavement shadow–affected cracks detection. Hence, this research aims to develop the illumination compensation model (ICM) and k-means clustering algorithm–based crack detection considering the influence of pavement shadows. First, the shadow area was divided into the umbra area and the penumbra area according to the illumination mechanism. Then, the shadow removal methods for different areas were analyzed separately. Since the intensity of the umbra shadow area changes homogeneously, the ICM approach can be a convenient way for shadow removal. While the intensity of penumbra area changes drastically, the cubic sample interpolation operation was conducted in advance, followed by ICM to finalize the shadow removal. After that, the k-means clustering algorithm was used to extract the crack region from the road background. Finally, based on the segmented binary crack image, the orientation, crack length, width, aspect ratio, area, and blocks were calculated for comprehensive crack-type classification and severity evaluation. Experiments were conducted to compare the performance of the proposed approach with traditional threshold segmentation, Poisson equation, contourlet transformation, and CrackTree, which demonstrated optimistic performance of the proposed method in terms of average precision (93.58%), recall (94.15%), and F-measure (93.86%).