A Lightweight and High-Accuracy Model for Pavement Crack Segmentation

Pavement cracks significantly affect road safety and longevity, making accurate crack segmentation essential for effective maintenance. Although deep learning methods have demonstrated excellent performance in this task, their large network architectures limit their applicability on resource-constrained devices. To address this challenge, this paper proposes a lightweight, fully convolutional neural network model, enhanced with spatial information. First, the backbone network structure is optimized to improve the efficiency of spatial information utilization. Second, by incorporating adaptive feature reassembly and wavelet transforms, the up-sampling and down-sampling processes are refined, enhancing the model capacity to capture spatial information. Lastly, a dynamic combined loss function is employed during training to further improve model attention on crack edge details. To validate the model performance, we trained and tested it on the Crack500 dataset and applied the trained model directly to the AsphaltCrack300 dataset. Experimental results indicate that the proposed model achieved an MIoU of 80.37% and an F1-score of 78.22% on the Crack500 dataset, representing increases of 3.08% and 5.62%, respectively, compared to EfficientNet. On the AsphaltCrack300 dataset, the model exhibited strong robustness, significantly outperforming other mainstream models. Additionally, its lightweight design provides clear advantages, making it well suited for realworld applications with limited computational resources.