ArticleScientific data2024
An annotated street view image dataset for automated road damage detection.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed.
- A fine-grained defect-sensitive local-global multiscale vision transformer framework for urban visual pollution prediction.Scientific reports · 2026Article
- Dynamic multi-scale fusion network for road damage detection in complex street views.Scientific reports · 2026Article
- An Improved YOLOv8 Model for Pavement Distress Detection Under Low-Computing-Power Conditions.Sensors (Basel, Switzerland) · 2026Article
- RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis.Scientific data · 2026Article
- A high-resolution perspective-view road image dataset for pothole detection.Scientific data · 2026Article
- Real-world road damage dataset with potholes, cracks, and maintenance holes.Scientific reports · 2026Article
- A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction.Scientific data · 2025Article
- URDD: An open dataset for urban roadway disease detection and classification.Data in brief · 2025Article
- Dataset for developing deep learning models to assess crack width and self-healing progress in concrete.Scientific data · 2025Article
- Dynamics of street environmental features and emotional responses in urban areas: implications for public health and sustainable development.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Road damage is a great threat to the service life and safety of roads, and the early detection of pavement damage can facilitate maintenance and repair. Street view images serve as a new solution for the monitoring of pavement damage due to their wide coverage and regular updates. In this study, a road pavement damage dataset, the Street View Image Dataset for Automated Road Damage Detection (SVRDD), was developed using 8000 street view images acquired from Baidu Maps. Based on these images, over 20,000 damage instances were visually recognized and annotated. These instances were distributed in five administrative districts of Beijing City. Ten well-established object detection algorithms were trained and assessed using the SVRDD dataset. The results have demonstrated the performances of these algorithms in the detection of pavement damages. To the best of our knowledge, SVRDD is the first public dataset based on street view images for pavement damages detection. It can provide reliable data support for future development of deep learning algorithms based on street view images.
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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.