Real-Time 3D Reconstruction of UAV Acquisition System for the Urban Pipe Based on RTAB-Map

Chen, Xinbao; Zhu, Xiaodong; Liu, Chang

doi:10.3390/app132413182

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…The YOLO series models, known for being single-stage algorithms, exhibit notably faster running performance. Among them, the YOLOv8 model stands out with the highest number of parameters (10.37×10 6 ) and minimal memory requirements, while achieving superior frame rates(132.38f•s -1 ) and training speed(2.6h) compared to the other two algorithms. When examining the same datasets, it is evident that the YOLOv8 model offers superior running performance and requires specific environment configurations.…”

Section: ) the Running Performance Of Autonomous Crack Detectionmentioning

confidence: 99%

“…Liu et al(2023[5]) proposed a systematic solution to automatic crack detection for UAV inspection with equipment of hardware and software,but the UAV lacks of autonomous ability. In addition, UAVs are gaining significance in the field of structural health performance inspections for civil engineering infrastructure, such pipeline inspection (Y C. etc.,2022 [3];Chen X etc.,2023 [6]) or bridge inspection (Metni, N etc.,2007.[7]).…”

Section: Introductionmentioning

confidence: 99%

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Pavement Crack Detection of UAV Autonomous Inspection System for Mountainous Roads

Xinbao,

Chenxi,

Chang

et al. 2024

Preprint

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Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

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Section: ) the Running Performance Of Autonomous Crack Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pavement Crack Detection of UAV Autonomous Inspection System for Mountainous Roads

Xinbao,

Chenxi,

Chang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, existing reconstruction methods [17][18][19][20][21][22][23][24], frequently omit images unsuitable for geometric modeling to mitigate the influence of redundant frames and images with excessively wide or narrow baselines on reconstruction accuracy. However, this curation process may lead to the exclusion of images containing vital texture information, thereby diminishing the quality of subsequent modeling.…”

Section: Introductionmentioning

confidence: 99%

Implicit–Explicit Coupling Enhancement for UAV Scene 3D Reconstruction

Lin,

2024

Applied Sciences

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In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric structures and textures. To address these challenges, an implicit–explicit coupling enhancement for a UAV large-scale scene modeling framework is proposed. Benefiting from the mutual promotion of implicit and explicit models, we initially address the issue of missing co-visibility clusters caused by environmental noise through large-scale implicit modeling with UAVs. This enhances the inter-frame photometric and geometric consistency. Subsequently, we enhance the multi-view point cloud reconstruction density via synthetic co-visibility clusters, effectively recovering missing spatial information and constructing a more complete dense point cloud. Finally, during the mesh modeling phase, high-quality 3D modeling of large-scale UAV scenes is achieved by inversely radiating and mapping additional texture details into 3D voxels. The experimental results demonstrate that our method achieves state-of-the-art modeling accuracy across various scenarios, outperforming existing commercial UAV aerial photography software (COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6) and related algorithms.

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Rapid 3D reconstruction of constant-diameter straight pipelines via single-view perspective projection

Yao,

Cheng,

Tan

et al. 2024

Front. Phys.

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Regular inspections of pipelines are of great significance to ensure their long-term safe and stable operation, and the rapid 3D reconstruction of constant-diameter straight pipelines (CDSP) based on monocular images plays a crucial role in tasks such as positioning and navigation for pipeline inspection drones, as well as defect detection on the pipeline surface. Most of the traditional 3D reconstruction methods for pipelines rely on marked poses or circular contours of end faces, which are complex and difficult to apply, while some existing 3D reconstruction methods based on contour features for pipelines have the disadvantage of slow reconstruction speed. To address the above issues, this paper proposes a rapid 3D reconstruction method for CDSP. This method solves for the spatial pose of the pipeline axis based on the geometric constraints between the projected contour lines and the axis, provided that the radius is known. These constraints are derived from the perspective projection imaging model of the single-view CDSP. Compared with traditional methods, the proposed method improves the reconstruction speed by 99.907% while maintaining similar accuracy.

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Real-Time 3D Reconstruction of UAV Acquisition System for the Urban Pipe Based on RTAB-Map

Cited by 4 publications

References 22 publications

Pavement Crack Detection of UAV Autonomous Inspection System for Mountainous Roads

Pavement Crack Detection of UAV Autonomous Inspection System for Mountainous Roads

Implicit–Explicit Coupling Enhancement for UAV Scene 3D Reconstruction

Rapid 3D reconstruction of constant-diameter straight pipelines via single-view perspective projection

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