Hu Yuanyuan, Chen Ning, Hou Yue, Lin Xingshi, Jing Baohong, Liu Pengfei
Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
Nat Commun. 2025 May 6;16(1):4212. doi: 10.1038/s41467-025-59516-5.
Efficient and accurate road distress detection is crucial for infrastructure maintenance and transportation safety. Traditional manual inspections are labor-intensive and time-consuming, while increasingly popular automated systems often rely on computationally intensive devices, limiting widespread adoption. To address these challenges, this study introduces MobiLiteNet, a lightweight deep learning approach designed for mobile deployment on smartphones and mixed reality systems. Utilizing a diverse dataset collected from Europe and Asia, MobiLiteNet incorporates Efficient Channel Attention to boost model performance, followed by structural refinement, sparse knowledge distillation, structured pruning, and quantization to significantly increase the computational efficiency while preserving high detection accuracy. To validate its effectiveness, MobiLiteNet improves the existing MobileNet model. Test results show that the improved MobileNet outperforms baseline models on mobile devices. With significantly reduced computational costs, this approach enables real-time, scalable, and accurate road distress detection, contributing to more efficient road infrastructure management and intelligent transportation systems.
高效且准确的道路病害检测对于基础设施维护和交通安全至关重要。传统的人工检查劳动强度大且耗时，而日益流行的自动化系统通常依赖计算密集型设备，限制了其广泛应用。为应对这些挑战，本研究引入了MobiLiteNet，这是一种专为在智能手机和混合现实系统上进行移动部署而设计的轻量级深度学习方法。利用从欧洲和亚洲收集的多样化数据集，MobiLiteNet采用高效通道注意力机制来提升模型性能，随后进行结构优化、稀疏知识蒸馏、结构化剪枝和量化，以在保持高检测精度的同时显著提高计算效率。为验证其有效性，MobiLiteNet改进了现有的MobileNet模型。测试结果表明，改进后的MobileNet在移动设备上优于基线模型。通过显著降低计算成本，这种方法能够实现实时、可扩展且准确的道路病害检测，有助于实现更高效的道路基础设施管理和智能交通系统。
