Surface Damage Detection and Localization for Bridge Visual Inspection Based on Deep Learning and 3D Reconstruction

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-07-29 DOI:10.1155/2024/9988793
Youhao Ni, Jianxiao Mao, Hao Wang, Zhuo Xi, Zhengyi Chen
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Abstract

In the process of infrastructure construction in recent decades, there exist millions of bridges in service that need safety inspection for performance assessment. Currently, computer vision and deep learning-based surface damage detection methods can achieve classification and localization of damages at the image level, but achieving precise localization in three-dimensional space is more challenging. To overcome aforementioned limitations, this study proposes a three-stage method of bridge surface damage detection and localization based on three-dimensional (3D) reconstruction. In stage 1, the UAV flight path planning of the bridge is carried out, and the 3D reconstruction model of the bridge is formed based on the structure from motion (SfM) algorithm. In stage 2, you-only-look-once version 7 (YOLOv7) network is adopted to detect multiple damages, and scale invariant feature transform (SIFT) detector is used to match the identical damage in image level. In stage 3, based on solution of epipolar geometric constraint, the matched damage was mapped to the 3D model, and the 3D damage localization is realized and visualized. The quality of the 3D model has been analyzed, and it is recommended that inspection distance is determined at 20 m. Moreover, the reconstruction model of bridges achieves centimeter-level positioning accuracy, and the positioning accuracy of damage reaches the meter level. The mapped model effectively showcases surface damages, providing bridge owners with intuitive insights.

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基于深度学习和三维重建的桥梁目测表面损伤检测与定位
在近几十年的基础设施建设过程中,有数百万座在役桥梁需要进行安全检测以评估性能。目前,基于计算机视觉和深度学习的表面损伤检测方法可以在图像层面实现损伤的分类和定位,但在三维空间实现精确定位更具挑战性。为克服上述局限性,本研究提出了一种基于三维(3D)重建的三阶段桥梁表面损伤检测和定位方法。在第一阶段,对桥梁进行无人机飞行路径规划,并基于运动结构(SfM)算法形成桥梁的三维重建模型。第 2 阶段,采用 YOLOv7 网络(you-only-look-once version 7,YOLOv7)检测多个损坏点,并使用尺度不变特征变换(SIFT)检测器在图像层面匹配相同的损坏点。在第三阶段,基于外极几何约束的求解,将匹配的损伤映射到三维模型中,实现三维损伤定位和可视化。分析了三维模型的质量,建议将检测距离确定为 20 米。此外,桥梁重建模型的定位精度达到了厘米级,损伤定位精度达到了米级。绘制的模型有效地展示了表面损伤,为桥梁业主提供了直观的见解。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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