ArticleSensors (Basel, Switzerland)2020
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2022Pooled it
- BS Dataset: A Tailor-Made Urban Road Pothole Dataset for Real-Time Detection and Safety-Oriented Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Edge Computing for Road Surface Classification Using Multi-IMU Data and a Hybrid CNN-LSTM Classification Model.Sensors (Basel, Switzerland) · 2026Article
- LRD-DETR: A Lightweight RT-DETR-Based Model for Road Distress Detection.Sensors (Basel, Switzerland) · 2026Article
- Modified MobileNetV2 transfer learning model to detect road potholes.PeerJ. Computer science · 2025Article
- Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification.Sensors (Basel, Switzerland) · 2024Article
- Article
- A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning.Sensors (Basel, Switzerland) · 2024Review
- Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of data representation techniques for vibration based road surface condition classification.Scientific reports · 2024Article
- A Road Defect Detection System Using Smartphones.Sensors (Basel, Switzerland) · 2024Article
- Detection of Road Potholes by Applying Convolutional Neural Network Method Based on Road Vibration Data.Sensors (Basel, Switzerland) · 2023Article
- On-Board Unit (OBU)-Supported Longitudinal Driving Behavior Monitoring Using Machine Learning Approaches.Sensors (Basel, Switzerland) · 2023Article
- Computer Vision Based Pothole Detection under Challenging Conditions.Sensors (Basel, Switzerland) · 2022Article
- Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Classification of the Sidewalk Condition Using Self-Supervised Transfer Learning for Wheelchair Safety Driving.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot.Sensors (Basel, Switzerland) · 2021Article
- Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.Computational intelligence and neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
Indexed as
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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.