This study introduces an integrated approach for road crack detection that harnesses the strengths of both visual transformers and CNN. A CNN is employed to capture fine-grained details, and a visual transformer is fully utilized to capture global characteristics. A feature fusion module is then designed to seamlessly merge the extracted features from both methods, thereby addressing the limitations of using CNN or visual transformer methods separately. Finally, the results are fed into an interactive decoder to produce accurate road crack detection results. Experimental results demonstrate that, whether on a publicly available or selfconstructed dataset, the proposed method demonstrates an improvement in performance as compared with using CNN or visual transformer methods separately for road crack detection tasks