A multimodal imaging textual fused channel spatial Kolmogorov–Arnold network for road crack detection

The accuracy of road crack detection is susceptible to factors such as illumination variations and tiny crack sizes. Additionally, the loss function in road crack detection is prone to fluctuations during the training process, leading to instability. To address these issues, this paper proposes a multimodal crack detection network to improve detection robustness and accuracy. First, a feature extraction module, named channel–spatial Kolmogorov–Arnold network (CSKAN), is designed specifically to handle challenges posed by illumination variations, enhancing the stability of feature extraction. (KAN is a mathematical framework that decomposes complex multivariate functions into learnable univariate components, enabling adaptive nonlinear modeling.) Second, an efficient multi-scale attention mechanism (Laplacian-adaptive weighted attention) based on the Laplace operator is introduced to address detection difficulties caused by tiny crack sizes and to improve the network's ability to capture small-scale crack features. Finally, a decoupling method for physical information neural networks (DPINNs) is proposed for the first time, ensuring better smoothness in the loss function during the training process. Experimental results demonstrate that our proposed detection framework outperforms seven state-of-the-art detection methods.