ArticleNature communications2025
Lightweight deep learning for real-time road distress detection on mobile devices.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Vehicle-Mounted Vision Sensing for Large-Scale Road Surface Monitoring: A Multi-Year Field Study of Acquisition Geometry and a Heterogeneous Detection-Classification Pipeline for Pothole Mapping.Sensors (Basel, Switzerland) · 2026Article
- RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis.Scientific data · 2026Article
- Static-electricity-induced luminescence trajectory tracking: A paradigm for noncontact human-machine interaction in dark environments.Science advances · 2026Article
- Lightweight vision architecture deployed in the terminal for safety monitoring and early warning of transmission lines.Nature communications · 2026Article
- Conquering camouflage: a collaborative feature refinement detector for near-color backgrounds and small-size challenges.Frontiers in plant science · 2026Article
- A Diffusion-Based Digital Twin for Predicting Road Deterioration under Climate and Traffic Stress.Research (Washington, D.C.) · 2026Article
- A multi-scale adaptive framework for high-precision rail track damage detection via StarNet and bidirectional feature pyramid network.Scientific reports · 2025Article
- Lightweight deep learning for real-time road distress detection on mobile devices.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.