Accurate identification of cracks at the pixel level on intricate asphalt pavements represents a crucial challenge in the domain of intelligent pavement assessment. The current advanced deep-learning networks encounter limitations in simultaneously capturing both the global context and local features of cracks, leading to discontinuous segmentation results and suboptimal recovery of local details. This paper proposes a robust architecture named Mix-Graph CrackNet to present an efficacious solution for this challenge. The Mix-Graph CrackNet, as proposed, is designed to mix the global context and local features multiple times, allowing for a comprehensively understanding of the essential features. Specifically, this paper develops the learnable parallel convolutional-Transformer mixing module to parallelly capture the sophisticated local features as well as the crucial global context. In addition, a new fusion unit is devised in the paper and deployed in the learnable parallel convolutional-Transformer mixing module. The proposed fusion unit is capable of effectively mixing contextual features extracted at both global and local scales while retaining an abundant level of textural details germane to the crack. Moreover, this paper constructs a graph-based skip connection that functions as a shortcut connecting the encoder and decoder, with the primary objective of mitigating information decay. The experimental results are remarkable, with the Mix-Graph CrackNet achieving F-measure and Intersection-Over-Union of 90.37% and 82.43%, respectively, on 1000 testing images. Based on the performance evaluations conducted on both public and private datasets, the proposed Mix-Graph CrackNet architecture demonstrates a significantly superior detection accuracy in comparison to several state-of-the-art models for semantic segmentation.
