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

2025-04-08

AI Summary

Conventional frame-based vision systems suffer from degraded performance in detecting civil infrastructure defects (e.g., cracks and spalling) under low-light or highly dynamic illumination conditions—common challenges for UAV-based inspection. Method: This work pioneers the integration of Dynamic Vision Sensors (DVS) into civil defect detection, introducing ev-CIVIL—the first event-camera dataset specifically designed for crack and spalling detection. It comprises synchronized event streams and grayscale frames captured across diverse field and laboratory scenarios using a DAVIS346 sensor. We systematically benchmark four real-time detectors (including YOLOv5 and NanoDet) on this dataset. Results: Event-based representations maintain high localization and classification accuracy under low-illumination and high-dynamic-range conditions, significantly outperforming frame-based baselines. This establishes a new paradigm for robust, low-power UAV inspection and provides the first standardized benchmark for event-driven civil infrastructure assessment.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.Four realtime object detection models were evaluated on it to validate the dataset effectiveness.The results demonstrate the dataset robustness in enabling accurate defect detection and classification,even under challenging lighting conditions.

Problem

• Detects civil structural defects using event-based cameras
• Addresses lack of dedicated event-based defect datasets
• Evaluates models for defect detection in varying lighting

Innovation

• Uses Dynamic Vision Sensors for defect detection
• Introduces first event-based civil defect dataset
• Combines DVS and APS sensors for data collection