# Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark

arXiv: https://arxiv.org/abs/2504.05679 · HTML: https://mlsanity.com/paper/2504.05679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 76 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [Object Detection with Spiking Neural Networks on Automotive Event Data](https://mlsanity.com/paper/2205.04339/) (2022)
- [From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection](https://mlsanity.com/paper/2304.13455/) (2023)
- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras](https://mlsanity.com/paper/2211.12324/) (2022)
- [Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset](https://mlsanity.com/paper/1904.08486/) (2019)
- [Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems](https://mlsanity.com/paper/1812.05659/) (2018)

## Built on by (most cited first, 2 of 2)

- [Real-Time Frame- and Event-based Object Detection with Spiking Neural Networks on Edge Neuromorphic Hardware: Design, Deployment and Benchmark](https://mlsanity.com/paper/2605.00146/) (2026)
- [How Does Urban Context Relate to Residential Building Health? A Vision-POI Fusion Framework for Building-Level Housing Inspection](https://mlsanity.com/paper/2607.20263/) (2026)

## Abstract (arXiv)

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 structural defects. Moreover, 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 using DVS. In addition 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 APS sensors. The dataset 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 spalling instances. The laboratory dataset includes 362 recording sequences, covering 220 distinct cracks and 308 spalling instances. We evaluated the dataset using four real-time object detection models.The results demonstrate the applicability of DVS cameras for robust detection of civil infrastructure defects under challenging lighting conditions.