Towards fully automated unmanned aerial vehicle-enabled bridge inspection: Where are we at?

Zhang, Cheng; Zou, Yang; Wang, Feng; Castillo, Enrique del Rey; Dimyadi, Johannes; Chen, L.

doi:10.1016/j.conbuildmat.2022.128543

Cited by 37 publications

(17 citation statements)

References 139 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the desired ground sampling distance (GSD), the necessary distance between the UAV and the object can be calculated as distance between camera and surface = GSD × Focal Length × Image Width Sensor Width (1) with the focal length and sensor width specified in millimeters and the image width given in pixels. The GSD is expressed as millimeters per pixel.…”

Section: Calculation Of Initial Camera Posesmentioning

confidence: 99%

“…In recent years, research related to the use of unmanned aerial vehicles (UAVs) equipped with cameras has steadily increased. As per the work of Zhang et al [1], the automated inspection process can be delineated into three phases: data acquisition via camera-equipped UAVs, data processing through automated damage detection software, and bridge condition assessment based on the determined damages. The primary emphasis of these investigations has centered on the second phase, automated damage detection, primarily leveraging deep learning methodologies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

Jung,

Merkle,

Reiterer

2024

Remote Sensing

View full text Add to dashboard Cite

This work explores the possibility of automating the aerial survey of bridges to generate high-resolution images necessary for digital damage inspection. High-quality unmanned aerial vehicle (UAV) based 3D reconstruction of bridges is an important step towards autonomous infrastructure inspection. However, the calculation of optimal camera poses remains challenging due to the complex structure of bridges and is therefore often conducted manually. This process is time-consuming and can lead to quality losses. Research in this field to automate this process is yet sparse and often requires high informative models of the bridge as the base for calculations, which are not given widely. Therefore, this paper proposes an automated camera pose calculation method solely based on an easily accessible polygon mesh of the bridge. For safe operation, point cloud data of the environment are used for automated ground detection and obstacle avoidance including vegetation. First, an initial set of camera poses is generated based on a voxelized mesh created in respect to the quality requirements for 3D reconstruction using defined camera specification. Thereafter, camera poses not fulfilling safety distances are removed and specific camera poses are added to increase local coverage quality. Evaluations of three bridges show that for diverse bridge types, near-complete coverage was achieved. Due to the low computational effort of the voxel approach, the runtime was kept to a minimum, even for large bridges. The subsequent algorithm is able to find alternative camera poses even in areas where the optimal pose could not be placed due to obstacles.

show abstract

Section: Calculation Of Initial Camera Posesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

Jung,

Merkle,

Reiterer

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Notably, HR crack images exhibit clearer crack boundaries than those from LR crack images, enhancing the accuracy of segmentation results and facilitating subsequent crack evaluation (Hoyer et al., 2022). Furthermore, during unmanned aerial vehicle (UAV)‐based bridge inspections, it has been observed that commercial UAVs must maintain a requisite distance from the bridge surface for safety considerations (Zhang, Zou et al., 2022). This safety distance primarily hinges on the wind velocity prevailing at the bridge location and the UAV's stability.…”

Section: Introductionmentioning

confidence: 99%

“…These areas encompass multiscale sampling, image rendering, multipath connections, physical cascading, a progressive recursive mechanism, conditional random fields (CRFs), and HR representation learning. However, all this research primarily concentrates on natural scene images characterized by regular sizes and morphological distributions (Kheradmandi & Mehranfar, 2022; Zhang et al., 2022). In contrast, cracks deviate from the objects found in natural scenes, as they are small objects exhibiting elongated topological structures and random morphological distributions (Ai et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network

Chu,

Chun

2023

Computer aided Civil Eng

View full text Add to dashboard Cite

High‐resolution (HR) crack images offer more detailed information for assessing structural conditions compared to low‐resolution (LR) images. This wealth of detail proves indispensable in bolstering the safety of unmanned aerial vehicle (UAV)‐based inspection procedures and elevating the precision of small crack segmentation. Nonetheless, achieving a balance between segmentation accuracy and GPU memory consumption poses a substantial challenge for deep learning models when processing HR crack images. To overcome this challenge, a novel “HR crack segmentation framework” (HRCSF) is proposed, specifically designed to meticulously segment crack images with resolutions exceeding 4K. First, a multiscale crack feature extraction network (MsCFEN) was proposed with the embedment of the strip pooling operation to enhance the representation of the transverse and longitudinal crack pixels from the complex backgrounds. Subsequently, two cascaded operations were tailored to MsCFEN, enabling a comprehensive refinement process that incorporates both global and local aspects. Furthermore, to fully leverage the potential of each proposed component in the refinement process, the complete architecture was trained using a loss function with embedded boundary optimization. Conclusively, a UAV‐based case study was conducted on a real bridge in Changsha, demonstrating HRCSF's practicability in segmenting HR crack images. The implementation of HRCSF allows the UAV to perform crack inspection effectively from a distance of 3 m away from the girder, resulting in a significant 50% reduction in inspection time compared to LR segmentation methods while maintaining high detection accuracy.

show abstract

“…LiDAR yields 3D data, but the accuracy and resolution are lower than 2D images of cameras. Thus, a fusion of data from two sensors may be optimal to detect damage and has been investigated previously (C. Zhang et al., 2022; Zhou et al, 2022). However, many prior studies on bridge damage detection used single sensors.…”

Section: Introductionmentioning

confidence: 99%

High‐resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery

Li,

Chen,

Liu

et al. 2023

Computer aided Civil Eng

View full text Add to dashboard Cite

Damage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor‐intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high‐performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image‐based and point‐cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two‐dimensional (2D) images and 3D LiDAR data, it outputs a high‐resolution 3D model and avoids environmental disturbances. After decision‐making‐level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually.

show abstract

Towards fully automated unmanned aerial vehicle-enabled bridge inspection: Where are we at?

Cited by 37 publications

References 139 publications

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

Automated Camera Pose Generation for High-Resolution 3D Reconstruction of Bridges by Unmanned Aerial Vehicles

Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network

High‐resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery

Contact Info

Product

Resources

About