Damage‐level classification considering both correlation between image and text data and confidence of attention map DOI
Keisuke Maeda, Naoki Ogawa, Takahiro Ogawa

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

Abstract In damage‐level classification, deep learning. models are more likely to focus on regions unrelated classification targets because of the complexities inherent in real data, such as diversity damages (e.g., crack, efflorescence, and corrosion). This causes performance degradation. To solve this problem, it is necessary handle data complexity uncertainty. study proposes a multimodal learning model that can damaged using text related damage images, materials components. Furthermore, by adjusting effect attention maps based confidence calculated when estimating these maps, proposed method realizes an accurate classification. Our contribution development with end‐to‐end mechanism simultaneously consider both image map. Finally, experiments images validate effectiveness method.

Language: Английский

Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments DOI Creative Commons
Tianyong Jiang, Lingyun Li, Bijan Samali

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: 39(23), P. 3646 - 3665

Published: April 25, 2024

Abstract To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage concrete bridges, this paper proposed an efficient lightweight damage model, constructed by improving you only look once v4 (YOLOv4) with MobileNetv3 fused inverted residual blocks, named YOLOMF. First, a novel network (MobileNetv3‐FusedIR) is as backbone This achieved integrating mobile bottleneck convolution (Fused‐MBConv) into shallow layers MobileNetv3. Second, standard YOLOv4 replaced depthwise separable convolution, resulting reduction number parameters complexity model. Third, effects different activation functions on performance YOLOMF are thoroughly investigated. Finally, to verify effectiveness method complex environments, data enhancement library Imgaug used simulate bridge images under challenging conditions such motion blur, fog, rain, snow, noise, color variations. The results indicate that shows excellent proficiency bridges across varying field‐of‐view sizes well environmental conditions. detection speed reaches 85f/s, facilitating effective real‐time environments.

Language: Английский

Citations

18
Crack image classification and information extraction in steel bridges using multimodal large language models DOI
Xiaodong Wang,

Qingrui Yue,

Xiaogang Liu

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 171, P. 105995 - 105995

Published: Jan. 28, 2025

Language: Английский

Citations

1
A data-driven prediction for concrete crack propagation path based on deep learning method DOI Creative Commons

Jiawei Lei,

Chengkan Xu, Chaofeng Lü

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: unknown, P. e03883 - e03883

Published: Oct. 1, 2024

Language: Английский

Citations

4
A DSF-net-based approach to dual-branch instance segmentation of weak bridge defects DOI
He Zhang,

Ruihong Shen,

Jiawei Lei

et al.

Engineering Structures, Journal Year: 2025, Volume and Issue: 327, P. 119583 - 119583

Published: Jan. 4, 2025

Language: Английский

Citations

0
Infrastructure automated defect detection with machine learning: a systematic review DOI Creative Commons
Saeed Talebi, Song Wu, Arijit Sen

et al.

International Journal of Construction Management, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: April 21, 2025

Language: Английский

Citations

0
A NURBS-based approach to the generation of geometric models for complex-shaped bridge using point clouds DOI

He Zhang,

Mindong Wu,

Tengxin Lin

et al.

Engineering Structures, Journal Year: 2025, Volume and Issue: 335, P. 120324 - 120324

Published: April 22, 2025

Language: Английский

Citations

0
Integrative AI and UAV-based visual recognition with metaheuristics for automated repair cost analysis of bridge structural deterioration DOI
Jui‐Sheng Chou, Jyh‐Ming Lien, Chi‐Yun Liu

et al.

Automation in Construction, Journal Year: 2025, Volume and Issue: 176, P. 106273 - 106273

Published: May 24, 2025

Language: Английский

Citations

0
Automatic steel girder inspection system for high‐speed railway bridge using hybrid learning framework DOI Open Access
Tao Xu, Yunpeng Wu, Yong Qin

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Abstract The steel girder of high‐speed railway bridges requires regular inspections to ensure bridge stability and provide a safe environment for operations. Unmanned aerial vehicle (UAV)‐based inspection has great potential become an efficient solution by offering superior perspectives mitigating safety concerns. Unfortunately, classic convolutional neural network (CNN) models suffer from limited detection accuracy or redundant model parameters, existing CNN‐based systems are only designed single visual task (e.g., bolt rust parsing only). This paper develops novel bi‐task (i.e., BGInet) recognize different types surface defects on UAV imagery. First, the assembles advanced branch that integrates sparse attention module, extended linear aggregation network, RepConv solve small object with scarce samples complete defect identification. Then, innovative U‐shape saliency is integrated into this system supplement parse regions. Smoothly, pixel‐to‐real‐world mapping utilizing critical flight parameters also developed assembled measure areas. Finally, extensive experiments conducted UAV‐based dataset show our method achieves better over current yet remains reasonably high inference speed. performance illustrates can effectively turn imagery useful information.

Language: Английский

Citations

2
A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation DOI Creative Commons
Zhicheng Zhang,

Zhijing Shen,

Jintong Liu

et al.

Sensors, Journal Year: 2023, Volume and Issue: 24(1), P. 3 - 3

Published: Dec. 19, 2023

The morphological characteristics of a crack serve as crucial indicators for rating the condition concrete bridge components. Previous studies have predominantly employed deep learning techniques pixel-level detection, while occasionally incorporating monocular devices to quantify dimensions. However, practical implementation such methods with assistance robots or unmanned aerial vehicles (UAVs) is severely hindered due their restrictions in frontal image acquisition at known distances. To explore non-contact inspection approach enhanced flexibility, efficiency and accuracy, binocular stereo vision-based method full convolutional network (FCN) proposed detecting measuring cracks. Firstly, our FCN leverages benefits encoder-decoder architecture enable precise segmentation simultaneously emphasizing edge details rate approximately four pictures per second database that dominated by complex background training results demonstrate precision 83.85%, recall 85.74% an F1 score 84.14%. Secondly, utilization vision improves shooting flexibility streamlines process. Furthermore, introduction central projection scheme achieves reliable three-dimensional (3D) reconstruction morphology, effectively avoiding mismatches between two views providing more comprehensive dimensional depiction An experimental test also conducted on cracked specimens, where relative measurement error width ranges from -3.9% 36.0%, indicating feasibility method.

Language: Английский

Citations

3
Weakly‐supervised structural component segmentation via scribble annotations DOI
Chenyu Zhang, Ke Li,

Zhaozheng Yin

et al.

Computer-Aided Civil and Infrastructure Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 30, 2024

Abstract Segmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential this task, existing methods typically require fully annotated ground truth masks, which are time‐consuming labor‐intensive to create. This paper introduces Scrib ble‐supervised Structural Comp onent Net work (ScribCompNet), the first weakly‐supervised method requiring only scribble annotations multiclass component segmentation. ScribCompNet features a dual‐branch architecture with higher‐resolution refinement enhance fine detail detection. It extends supervision from labeled unlabeled pixels through combined objective function, incorporating annotation, dynamic pseudo label, semantic context enhancement, scale‐adaptive harmony losses. Experimental results show that outperforms other scribble‐supervised most fully‐supervised counterparts, achieving 90.19% mean intersection over union (mIoU) an 80% reduction labeling time. Further evaluations confirm effectiveness novel designs robust performance, even lower‐quality annotations.

Language: Английский

Citations

0