ArticleComputational intelligence and neuroscience2021
Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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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
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2022Pooled it
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- Attention-Fused Staged DWT-LSTM for Fault Diagnosis of Embedded Sensors in Asphalt Pavement.Materials (Basel, Switzerland) · 2025Article
- NeoStarling: An Efficient and Scalable Collaborative Blockchain-Enabled Obstacle Mapping Solution for Vehicular Environments.Sensors (Basel, Switzerland) · 2023Article
- Using Machine Learning on V2X Communications Data for VRU Collision Prediction.Sensors (Basel, Switzerland) · 2023Article
- Modeling an Edge Computing Arithmetic Framework for IoT Environments.Sensors (Basel, Switzerland) · 2022Article
- Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model.Journal of healthcare engineering · 2022Article
- An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks.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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.
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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.