Small Unmanned Aerial Vehicle (UAV) based visual inspections are a more efficient alternative to manual methods for examining civil structural defects, offering safe access to hazardous areas and significant cost savings by reducing labor requirements. However, traditional frame-based cameras, widely used in UAV-based inspections, often struggle to capture defects under low or dynamic lighting conditions. In contrast, Dynamic Vision Sensors (DVS), or event-based cameras, excel in such scenarios by minimizing motion blur, enhancing power efficiency, and maintaining high-quality imaging across diverse lighting conditions without saturation or information loss. Despite these advantages, existing research lacks studies exploring the feasibility of using DVS for detecting civil structuralthis http URL, there is no dedicated event-based dataset tailored for this purpose. Addressing this gap, this study introduces the first event-based civil infrastructure defect detection dataset, capturing defective surfaces as a spatio-temporal event stream usingthis http URLaddition to event-based data, the dataset includes grayscale intensity image frames captured simultaneously using an Active Pixel Sensor (APS). Both data types were collected using the DAVIS346 camera, which integrates DVS and APSthis http URLdataset focuses on two types of defects: cracks and spalling, and includes data from both field and laboratory environments. The field dataset comprises 318 recording sequences,documenting 458 distinct cracks and 121 distinct spallingthis http URLlaboratory dataset includes 362 recording sequences, covering 220 distinct cracks and 308 spallingthis http URLrealtime object detection models were evaluated on it to validate the datasetthis http URLresults demonstrate the dataset robustness in enabling accurate defect detection and classification,even under challenging lighting conditions.
View on arXiv@article{gamage2025_2504.05679,
title={ Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark },
author={ Udayanga G.W.K.N. Gamage and Xuanni Huo and Luca Zanatta and T Delbruck and Cesar Cadena and Matteo Fumagalli and Silvia Tolu },
journal={arXiv preprint arXiv:2504.05679},
year={ 2025 }
}