A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection

Agnisarman, Sruthy; Lopes, Snowil; Madathil, Kapil Chalil; Piratla, Kalyan R.; Gramopadhye, Anand K.

doi:10.1016/j.autcon.2018.10.019

Cited by 136 publications

(64 citation statements)

References 50 publications

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“…However, this study [16] did not provide an in-depth exploration of the salient features of the automation solutions and their applications for construction automation. Another relevant review study proposed by Agnisarman [17] focused on human-in-the-loop-based visual inspection systems for construction automation. There are a number of ways in which this study has a limited scope, as: (i) it does not focus on robot-based semi-autonomous and autonomous applications for construction automation, and (ii) this study only examined visual inspection-based sensors for SHM [17].…”

Section: Comparison With Existing Reviewsmentioning

confidence: 99%

“…Another relevant review study proposed by Agnisarman [17] focused on human-in-the-loop-based visual inspection systems for construction automation. There are a number of ways in which this study has a limited scope, as: (i) it does not focus on robot-based semi-autonomous and autonomous applications for construction automation, and (ii) this study only examined visual inspection-based sensors for SHM [17]. Similarly, another review-based study also provided an examination of vision-based applications for construction automation [18].…”

Section: Comparison With Existing Reviewsmentioning

confidence: 99%

“…Collectively, the different topics explored by the existing review studies include the discussion related to various NDE sensors, the sensor fusion techniques in general for all applications and particularly for NDE and algorithms for SHM-related data analysis. [19] Liu [14] Khaleghi [15] Sakagami [13] Rehman [20] Chen [16] Feng [22] Rakha [21] Agnisarman [17] Taheri [23] Martinez [18] Dabous [24] This review…”

Section: Dabous Et Al [24] 2020mentioning

confidence: 99%

“…In this review, the focus will be towards examining some of the recent technological developments in relation to the automation of NDE for SHM culminating in various single-sensor and multi-sensor systems with semi/full autonomous capabilities [8,12,[16][17][18]. This review-based study will also cover some of the most important NDE sensors (e.g., visual sensors, impact-echo (IE), infrared (IR), ground-penetrating radar (GPR) and electrical resistivity (ER) sensors) and relevant studies utilizing data from aforementioned sensors towards the NDE of bridges in particular.…”

Section: Of 38mentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Comparison With Existing Reviewsmentioning

confidence: 99%

Section: Comparison With Existing Reviewsmentioning

confidence: 99%

Section: Dabous Et Al [24] 2020mentioning

confidence: 99%

Section: Of 38mentioning

confidence: 99%

See 2 more Smart Citations

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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“…Studies have tried to extract useful features from drone image data [25], LIDAR point cloud data [30], Google street view image data [31], 3D laser profilers [32,33], 3D laser images [34] and normal 2D images [35]. There have also been attempts to utilize convolutional neural networks for the purpose of segmenting pavement images using annotated masks on the images [36].…”

Section: The Use Of Image Segmentation In Pavement Condition Evaluationsmentioning

confidence: 99%

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020

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Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS.

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Automatic Detection and Its Applications in Infrastructure

2022

Automation and Computational Intelligence for Road Maintenance and Management

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A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection

Cited by 136 publications

References 50 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

Automatic Detection and Its Applications in Infrastructure

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