ASPCCNet: A Lightweight Pavement Crack Classification Network Based on Augmented ShuffleNet

Pavement cracks are a critical indicator for assessing structural health and forecasting deterioration trends. Accurate and automated crack classification is of paramount importance for the intelligent maintenance of road structures. Inspired by the principles of symmetry—which often lead to robust and efficient structures in both nature and engineering—this paper proposes ASPCCNet, a lightweight network that embeds these principles into its core design. The network centers on a novel building block, AugShuffleBlock, which embodies a symmetry-informed design through the integration of Partial Convolution (PConv), a tunable channel splitting mechanism (AugShuffle), and the Channel Prior Convolutional Attention (CPCA). This design achieves efficient feature extraction and fusion with minimal computational overhead. Experimental results on the public RCCD dataset demonstrate that ASPCCNet significantly outperforms mainstream lightweight models, achieving an F1-score of 0.816, which is 6.4% to 10.9% higher than other mainstream models, with only 0.294 M parameters and 48.68 MFLOPs. This work showcases how a symmetry-guided design philosophy can be leveraged to achieve a superior balance between accuracy and efficiency for real-time edge deployment.