Optimizing Pavement Distress Detection with UAV: A Comparative Study of Vision Transformer and Convolutional Neural Networks DOI Creative Commons
Yao Zhang, Jiajun Chen, Zheng‐Guang Wu

et al.

KSCE Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 100095 - 100095

Published: Oct. 1, 2024

Language: Английский

An end-to-end computer vision system based on deep learning for pavement distress detection and quantification DOI Creative Commons
Saúl Cano-Ortiz, L. Lloret Iglesias, P. Martínez Ruiz del Árbol

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 416, P. 135036 - 135036

Published: Feb. 1, 2024

The performance of deep learning-based computer vision systems for road infrastructure assessment is hindered by the scarcity real-world, high-volume public datasets. Current research predominantly focuses on crack detection and segmentation, without devising end-to-end capable effectively evaluating most affected roads assessing out-of-sample performance. To address these limitations, this study proposes a dataset with annotations 7099 images 13 types defects, not only based cracks, confrontation development learning models. These are used to train compare YOLOv5 sub-models pure efficiency, standard object metrics, select optimum architecture. A novel post-processing filtering mechanism then designed, which reduces false positive detections 20.5%. Additionally, pavement condition index (ASPDI) engineered models identify areas in need immediate maintenance. facilitate decision-making administrations, software application created, integrates ASPDI, geotagged images, detections. This tool has allowed detect two sections critical repair. refined architecture validated open datasets, achieving mean average precision scores 0.563 0.570 RDD2022 CPRI, respectively.

Language: Английский

Citations

13
A lightweight detection method of pavement potholes based on binocular stereo vision and deep learning DOI
Chao Xing, Guiping Zheng, Yongkang Zhang

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 436, P. 136733 - 136733

Published: June 7, 2024

Language: Английский

Citations

6
UAV based sensing and imaging technologies for power system detection, monitoring and inspection: a review DOI
Yunze He, Zhaoyan Liu,

Yike Guo

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 68

Published: Nov. 22, 2024

With the advancement and expansion of power system technology, ensuring safe stable operation transmission lines has become increasingly crucial. Traditional manual inspection methods often suffer from low efficiency high risks. In recent years, rapid development unmanned aerial vehicle (UAV) technology provided a new solution for inspection. UAV offers advantages such as flexibility, wide coverage, relatively overall costs, effectively improving efficiency, reducing mitigating personnel safety A comprehensive review current state inspections in systems is this study, introducing main sensing technologies applications inspection, including visible light cameras, infrared thermal imaging, depth compound eye radar, X-rays, ultrasonic sensors. It also discusses challenges faced by systems, complexity data processing, lack automation, absence regulatory frameworks. Furthermore, it put forward future trends, multi-sensor fusion, edge computing, cloud computing centres, multi-UAV collaboration, cooperation various clusters. This work aims to serve reference research application systems.

Language: Английский

Citations

5
Semi-supervised crack detection using segment anything model and deep transfer learning DOI
Jiale Li,

Chenglong Yuan,

Xuefei Wang

et al.

Automation in Construction, Journal Year: 2024, Volume and Issue: 170, P. 105899 - 105899

Published: Dec. 11, 2024

Language: Английский

Citations

4
EF-RT-DETR: a efficient focused real-time DETR model for pavement distress detection DOI
Tao Han, S. Y. Hou, Chenyang Gao

et al.

Journal of Real-Time Image Processing, Journal Year: 2025, Volume and Issue: 22(2)

Published: Feb. 19, 2025

Language: Английский

Citations

0
A novel method for pothole detection based on incomplete point clouds DOI
Junkui Zhong, Deyi Kong, Yuliang Wei

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117344 - 117344

Published: March 1, 2025

Language: Английский

Citations

0
Deeep Learning in Crack Detection: A Comprehensive Scientometric Review DOI Creative Commons

Yingjie Wu,

Shaoqi Li,

Jingqiu Li

et al.

Journal of Infrastructure Intelligence and Resilience, Journal Year: 2025, Volume and Issue: unknown, P. 100144 - 100144

Published: Feb. 1, 2025

Language: Английский

Citations

0
Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation DOI Open Access

Dingping Chen,

Zhiheng Zhu,

Jinyang Fu

et al.

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2024, Volume and Issue: 79(1), P. 1679 - 1703

Published: Jan. 1, 2024

The detection of crack defects on the walls road tunnels is a crucial step in process ensuring travel safety and performing routine tunnel maintenance.The automatic accurate cracks surface key to improving maintenance efficiency tunnels.Machine vision technology combined with deep neural network model an effective means realize localization identification tunnels.We propose complete set inspection methods for identifying as solution problem difficulty during manual maintenance.First, equipment applied real-time acquisition high-definition images designed.Images are acquired based designed equipment, where containing manually identified selected.Subsequently, training validation sets used construct obtained images, whereas regions pixels finely labeled.After that, area sensing module proposed you only look once version 7 coordinate attention mechanism (CA-YOLO V7) locate images.Only subimages sent multiscale semantic segmentation extraction which belong DeepLab V3+ network.The precision recall region our method 82.4% 93.8%, respectively.Moreover, mean intersection over union (MIoU) pixel accuracy (PA) values achieving pixel-level 76.84% 78.29%, respectively.The experimental results dataset show that two-stage outperforms other state-of-the-art models detection.Based method, captured can at speed ten frames/second, reach 0.25 mm, meets requirements actual project.The CA-YOLO V7 enables precise belongs under different environmental lighting conditions tunnels.The improved lightweighting able extract morphology given more quickly while maintaining accuracy.The established combines defect first time, realizing complex environments, capable determining size physical system after camera calibration.The trained has high be extended embedded computing devices assessment repair damaged areas types tunnels.

Language: Английский

Citations

2
Research on the detection and identification method of internal cracks in semi-rigid base asphalt pavement based on three-dimensional ground penetrating radar DOI
Haoran Zhu, Guofang Wei,

Dongsheng Ma

et al.

Measurement, Journal Year: 2024, Volume and Issue: unknown, P. 116486 - 116486

Published: Dec. 1, 2024

Language: Английский

Citations

2
Optimizing Pavement Distress Detection with UAV: A Comparative Study of Vision Transformer and Convolutional Neural Networks DOI Creative Commons
Yao Zhang, Jiajun Chen, Zheng‐Guang Wu

et al.

KSCE Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 100095 - 100095

Published: Oct. 1, 2024

Language: Английский

Citations

1