SynthesisSensors (Basel, Switzerland)2023
Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- A Macro-to-Micro Framework for Bridge-Deck Pavement-Distress Inspection Using LiDAR-Based Screening and YOLOv8s Image Detection: A Case Study of the Jingzhou Yangtze River Highway Bridge and Public Benchmark Data.Sensors (Basel, Switzerland) · 2026Article
- LiDAR-Based Road Surface Damage Classification: A Survey.Sensors (Basel, Switzerland) · 2026Review
- 3D Road Defect Mapping via Differentiable Neural Rendering and Multi-Frame Semantic Fusion in Bird's-Eye-View Space.Journal of imaging · 2026Article
- SIFT-SNN for Traffic-Flow Infrastructure Safety: A Real-Time Context-Aware Anomaly Detection Framework.Journal of imaging · 2026Article
- Deep learning and TOPSIS-based multi-criteria decision-making framework for urban road defect detection and sustainable maintenance planning.Scientific reports · 2025Article
- An intelligent YOLO and CNN-BiGRU framework for road infrastructure based anomaly assessment.Scientific reports · 2025Article
- Exploring single-head and multi-head CNN and LSTM-based models for road surface classification using on-board vehicle multi-IMU data.Scientific reports · 2025Article
- Innovative Driver Monitoring Systems and On-Board-Vehicle Devices in a Smart-Road Scenario Based on the Internet of Vehicle Paradigm: A Literature and Commercial Solutions Overview.Sensors (Basel, Switzerland) · 2025Review
- Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of data representation techniques for vibration based road surface condition classification.Scientific reports · 2024Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD's state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.
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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.