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Vision Based Sensors and Sensing Technologies for Structural Health Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 7219

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Special Issue Editor

Dr. Mohammad Jahanshahi

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Guest Editor

Purdue University
Interests: computer vision; structural health monitoring; artificial intelligence; robotic inspection

Special Issue Information

Dear colleagues,

Due to recent advances in sensors and computing technologies, the use of vision-based sensing technologies provides an unprecedented opportunity to complement traditional structural health monitoring (SHM) and nondestructive evaluation (NDE) approaches. Moreover, vision methods are generally contactless and appropriate to be incorporated in mobile sensing robots, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), providing a transformative monitoring platform for structures. This Special Issue will provide the opportunity to present recent theoretical, computational, and experimental advances in using advanced vision-based sensing technologies, computer vision, and machine learning approaches for structural identification, control, damage detection, and health monitoring. Topics relevant to this Special Issue include include but are not limited to multimodal sensing, deep learning, innovative imaging for structures, image/video data collection and analysis, damage detection, classification, convolutional neural networks, network pruning, quantification and localization, change recognition, displacement and dynamic measurements, sensor calibration, sensor fusion and optimization, scene reconstruction, 3D LIDAR and depth sensors, robotic inspection, vision-based inspection using UAVs and UGVs, and other new emerging vision-based technologies.

Dr. Mohammad Jahanshahi
Guest Editor

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Keywords

vision-based sensors
computer vision
image processing
data analytics

Published Papers (3 papers)

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Research

22 pages, 7486 KiB

Open AccessArticle

An Automated Pipeline for Dynamic Detection of Sub-Surface Metal Loss Defects across Cold Thermography Images

by Siavash Doshvarpassand and Xiangyu Wang

Sensors 2021, 21(14), 4811; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144811 - 14 Jul 2021

Cited by 3 | Viewed by 1618

Abstract

Utilising cooling stimulation as a thermal excitation means has demonstrated profound capabilities of detecting sub-surface metal loss using thermography. Previously, a prototype mechanism was introduced which accommodates a thermal camera and cooling source and operates in a reciprocating motion scanning the test piece while cold stimulation is in operation. Immediately after that, the camera registers the thermal evolution. However, thermal reflections, non-uniform stimulation and lateral heat diffusions will remain as undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no prior knowledge of the non-defective area in order to effectively distinguish between defective and non-defective areas. In this work, the previously automated acquisition and processing pipeline is re-designed and optimised for two purposes: 1—Through the previous work, the mentioned pipeline was used to analyse a specific area of the test piece surface in order to reconstruct the reference area and identify defects. In order to expand the application of this device over the entire test area, regardless of its extension, the pipeline is improved in which the final surface image is reconstructed by taking into account multiple segments of the test surface. The previously introduced pre-processing method of Dynamic Reference Reconstruction (DRR) is enhanced by using a more rigorous thresholding procedure. Principal Component Analysis (PCA) is then used in order for feature (DRR images) reduction. 2—The results of PCA on multiple segment images of the test surface revealed different ranges of intensities across each segment image. This potentially could cause mistaken interpretation of the defective and non-defective areas. An automated segmentation method based on Gaussian Mixture Model (GMM) is used to assist the expert user in more effective detection of the defective areas when the non-defective areas are uniformly characterised as background. The final results of GMM have shown not only the capability of accurately detecting subsurface metal loss as low as 37.5% but also the successful detection of defects that were either unidentifiable or invisible in either the original thermal images or their PCA transformed results. Full article

(This article belongs to the Special Issue Vision Based Sensors and Sensing Technologies for Structural Health Monitoring)

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20 pages, 14408 KiB

Open AccessArticle

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

by Leanne Attard, Carl James Debono, Gianluca Valentino and Mario Di Castro

Sensors 2021, 21(12), 4040; https://0-doi-org.brum.beds.ac.uk/10.3390/s21124040 - 11 Jun 2021

Cited by 10 | Viewed by 2216

Abstract

Tunnel structural health inspections are predominantly done through periodic visual observations, requiring humans to be physically present on-site, possibly exposing them to hazardous environments. These surveys are subjective (relying on the surveyor experience), time-consuming, and may demand operation shutdown. These issues can be mitigated through accurate automatic monitoring and inspection systems. In this work, we propose a remotely operated machine vision change detection application to improve the structural health monitoring of tunnels. The vision-based sensing system acquires the data from a rig of cameras hosted on a robotic platform that is driven parallel to the tunnel walls. These data are then pre-processed using image processing and deep learning techniques to reduce nuisance changes caused by light variations. Image fusion techniques are then applied to identify the changes occurring in the tunnel structure. Different pixel-based change detection approaches are used to generate temporal change maps. Decision-level fusion methods are then used to combine these change maps to obtain a more reliable detection of the changes that occur between surveys. A quantitative analysis of the results achieved shows that the proposed change detection system achieved a recall value of 81%, a precision value of 93% and an F1-score of 86.7%. Full article

(This article belongs to the Special Issue Vision Based Sensors and Sensing Technologies for Structural Health Monitoring)

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18 pages, 7638 KiB

Open AccessArticle

Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

by Billur Kazaz, Subhadipto Poddar, Saeed Arabi, Michael A. Perez, Anuj Sharma and J. Blake Whitman

Sensors 2021, 21(8), 2834; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082834 - 17 Apr 2021

Cited by 15 | Viewed by 2351

Abstract

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices. Full article

(This article belongs to the Special Issue Vision Based Sensors and Sensing Technologies for Structural Health Monitoring)

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