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Abstract: The deteriorating condition of road infrastructure poses a significant challenge to public safety and transportation efficiency. This research presents a robust approach to road crack detection leveraging advanced deep learning techniques. Our proposed model combines the power of attention mechanisms and residual structures within a deep neural network architecture to enhance the accuracy and efficiency of road crack identification. The attention mechanism enables the model to focus on crucial features while disregarding irrelevant information, facilitating a more precise localization of road cracks. Concurrently, the integration of residual structures aids in mitigating the vanishing gradient problem, allowing for the effective training of deeper networks. We employ a diverse dataset containing various road surfaces, lighting conditions, and crack types to ensure the model's generalization capability. The training process involves optimizing the network's parameters using an appropriate loss function, leading to a highly discriminative model for road crack detection. This research contributes to the advancement of intelligent transportation systems by providing an efficient and reliable solution for automated road crack detection. The proposed deep neural network offers great potential for integration into smart infrastructure maintenance systems, ultimately contributing to the enhancement of road safety and durability.