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Lightweight deep learning for real-time road distress detection on mobile devices

Yuanyuan Hu Orcid Logo, Ning Chen, Yue Hou Orcid Logo, Xingshi Lin, Baohong Jing, Pengfei Liu Orcid Logo

Nature Communications, Volume: 16, Start page: 4212

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1038/s41467-025-59516-5

Abstract

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...

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Published in: Nature Communications
ISSN: 2041-1723
Published: Springer Nature 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69449
Abstract: 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.
College: Faculty of Science and Engineering
Funders: Open Access funding enabled and organized by Projekt DEAL.
Start Page: 4212

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