The detection of pavement cracks is essential for damage assessment and maintenance of pavement. Obtaining complete crack paths using traditional approaches is difficult due to the varied appearance of pavement cracks and complex texture noise. A robust graph network refining algorithm guided by multiscale curvilinear structure filtering (CFGNR) is proposed for pavement crack detection. A multiscale curvilinear structure filter consisting of curved linear templates and a local texture inhibition term is first utilized to enhance crack contours. The enhanced pavement image is then presented as a graph of overcomplete crack paths, and a graph network refining approach derived from path saliency and local contrast constraints is utilized to select the optimal subset of crack paths. Finally, an iterative path growing algorithm is employed to obtain pixel-level cracks. Experimental results on four public pavement datasets show that the proposed algorithm significantly improves the completeness of detected cracks and achieves a superior performance compared to six existing algorithms.