Rapid Surface Damage Detection Equipment for Subway Tunnels Based on Machine Vision System

Huang, Zhen; Fu, Helin; Fan, Xiaopeng; Meng, Jiaqi; Chen, Wei; Zheng, Xiaojun; Wang, Fei; Zhang, Jiabing

doi:10.1061/(asce)is.1943-555x.0000591

Cited by 17 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors claimed that the proposed model performs better than FCN, SegNet, DeepLab v3+, and SDDNet. Huang et al [95] proposed a software system for damage detection in subway tunnels by integrating four separate functions: image fusion to splice the images acquired by different cameras, image preprocessing to remove background noise and other preprocessing tasks, damage identification performed by the R-CNN model and a data platform for evaluation by the respective personnel. Arya et al [96] proposed a concrete pavement damage dataset consisting of 26,620 data point from multiple countries and investigated how the demographics of the damage data affect the model performance based on a YOLO-v5/YOLO-v4/cascade R-CNN-based ensemble model.…”

Section: Damage Identificationmentioning

confidence: 99%

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

Chowdhury,

Kaiser

2024

Construction Materials

View full text Add to dashboard Cite

Concrete stands as the most widely used construction material globally due to its versatility, encompassing applications ranging from pavement, multifloor structures, and bridges to dams. However, these concrete structures endure structural stress and require close monitoring to prevent accidents and ensure sustainability throughout their complete life cycle. In recent years, artificial intelligence (AI) and computer vision (CV) have demonstrated considerable potential in diverse applications within construction engineering, including structural health monitoring (SHM) and inspection processes such as crack and damage detection, as well as rebar exposure. While it is undeniable that CV and deep learning models are transforming the construction industry by offering robust solutions for complex scenarios, there remain numerous challenges pertinent to their applications that require attention. This paper aims to systematically and critically review the literature of the past decade on the application of deep learning models in the construction industry for SHM purposes in concrete structures. The review delves into proposed methodologies and technologies while identifying opportunities and challenges associated with these applications in practice. Additionally, the paper provides insights to bridge the gap between theory and application.

show abstract

Section: Damage Identificationmentioning

confidence: 99%

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

Chowdhury,

Kaiser

2024

Construction Materials

View full text Add to dashboard Cite

show abstract

“…2,3 The impressive and rapid progress of deep learning algorithms in computer vision motivates many researchers to apply such algorithms to vision-based SHM applications. [4][5][6] Specifically, the civil infrastructure condition assessment tasks, including critical structural components recognition and structural damage detection that use images as input are closely related to the classic computer vision tasks, including image classification, object detection, and semantic segmentation. The key difference among these tasks and the corresponding approaches lies in the level of details of recognition, which are image level, region level, and pixel level from coarse to fine.…”

Section: Introductionmentioning

confidence: 99%

A novel transformer-based semantic segmentation framework for structural condition assessment

Wang

Shao

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

Conventional structural health monitoring (SHM) evaluates the condition of civil structures by analyzing the data acquired by advanced sensors. The requirement of overinvestment in specialized equipment and labor for implementation prevents the traditional SHM from large-scale usage. On the other hand, computer vision techniques offer cost-effective solutions for SHM thanks to its inherent advantage in data acquirement and processing. More importantly, it has been demonstrated that these emerging solutions can produce reliable condition diagnoses for civil structures using pure image data. In this article, a novel transformer-based neural network is proposed for vision-based structural condition assessment which is formulated to a semantic segmentation problem. The network employs Swin Transformer as the backbone and MaskFormer as the overall architecture to recognize components (sleepers, slabs, columns, etc.) and damage (concrete damage, exposed rebar) of structures. Unlike the commonly used fully convolutional networks, the proposed model tackles semantic segmentation as a mask classification rather than a pixel classification problem. To deal with the lack of training data, an image data augmentation method called Copy-Paste is extended and applied for training data generation, resulting in an increase of around 40% data for component segmentation and 71% data for damage segmentation. Experimental validations on the Tokaido railway viaduct dataset show that the proposed approach is very accurate, achieving 97% and 90% mean Intersection Over Union for component and damage segmentation, outperforming the existing methods by a significant margin. The accurate segmentation results can provide meaningful information for downstream SHM tasks.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Moreover, tunnel underwater structural defect images suffer from low contrast and single tone, which brings a huge challenge to applying IPT methods for tunnel structural defect detection. 25 Recently, with the rapid development of artificial intelligence (AI), deep learning (DL) combined with machine vision (MV) are introduced for tunnel structural damage detection. For instance, Xue et al 26 proposed a fast detection method for shielding tunnel lining defects using a Region-based fully convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

An integrated underwater structural multi-defects automatic identification and quantification framework for hydraulic tunnel via machine vision and deep learning

Bao

Huang

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Underwater structural defects in hydraulic tunnels are highly concealed and difficult to be identified by conventional manual methods. Remotely operated vehicle combined with visible light cameras can provide a noncontact and high spatial resolution damage detection solution. However, manually extracting useful structural damage-related information from massive data is time-consuming and involves high labor cost. This article proposes an integrated pixel-level underwater structural multi-defects instance segmentation and quantification framework for hydraulic tunnels via machine vision and deep learning. Firstly, a tunnel lining underwater structural multi-defects video dataset is developed. Next, an improved You Only Look At CoefficienTs for Edge devices is used to build the detector by exploiting temporal redundancy in videos. Three backbone detectors are used to trade off the balance between detection accuracy and efficiency, and a cross-domain transfer learning strategy is introduced to reduce model training costs and data dependencies. Various complicated tunnel underwater inspection scenarios, including uneven illumination, tilt shooting, high brightness, and motion blur scenarios, are used to evaluate model generalization capability. Experimental results show that ResNet50-based YolactEdge can well trade off the balance between accuracy and speed, which achieves 92.47 bbox mAP, 92.15 mask mAP, and 39.27 FPS in the testing set. A quantification evaluation method is proposed to quantify the detection results and extract the geometric features of structural defects based on digital image processing techniques. The proposed method can accurately identify the number, size, and area of tunnel underwater structural defects, providing data support for subsequent reinforcement.

show abstract

Rapid Surface Damage Detection Equipment for Subway Tunnels Based on Machine Vision System

Cited by 17 publications

References 19 publications

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

A novel transformer-based semantic segmentation framework for structural condition assessment

An integrated underwater structural multi-defects automatic identification and quantification framework for hydraulic tunnel via machine vision and deep learning

Contact Info

Product

Resources

About