Quantitative Evaluation of CHT and GHT for Column Detection under Different Conditions of Data Quality

Bueno, M.; Díaz-Vilariño, L.; González-Jorge, H.; Martínez-Sánchez, J.; Arias, Pedro

doi:10.1061/(asce)cp.1943-5487.0000678

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…All these parameters have been supported by studies performed by Ishida et al (2012), Hajian and Brandow (2012), Turkan et al (2013), Wang et al (2017), Maalek et al (2019), Kim et al (2020) and Kim et al (2021). The effectiveness and accuracy of scanned data are very much dependent on scan distance (Kim et al, 2016) and scanning object shape (Bueno et al, 2017). Likewise, researchers have also highlighted rebar spacing as an effective parameter to be considered while scanning (Kim et al, 2020) and occlusions as a hurdle to achieve effective results in laser scanning (Ad an et al, 2019).…”

Section: Discussionmentioning

confidence: 89%

Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Qureshi

Alaloul²,

Wing³

et al. 2022

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Purpose Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders. Design/methodology/approach A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index. Findings The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”. Originality/value The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

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Section: Discussionmentioning

confidence: 89%

Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Qureshi

Alaloul²,

Wing³

et al. 2022

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“…The study found that the Hough Transform algorithm is sensitive to the circumference of columns. In addition, Bueno et al [27] examined the Hough Transform algorithm reliability using simulated column data. The study shows that completeness of edges is the most sensitive variable affecting the performance of the Hough transform algorithm and it was found that at least 30% to 40% edge completeness is required to robustly estimate the radius of columns.…”

Section: Circle Fitting Applications In the Construction Industrymentioning

confidence: 99%

Automated rebar diameter classification using point cloud data based machine learning

Kim

Thedja

Chi

et al. 2021

Automation in Construction

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“…Díaz-Vilariño et al [8] presented a methodology for detecting and segmenting round and rectangular cross-section columns in as-built buildings from point clouds. A similar approach was evaluated by Bueno et al [9] under different conditions of quality and completeness.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos

Díaz-Vilariño

Balado

et al. 2018

IJGI

Self Cite

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This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

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Quantitative Evaluation of CHT and GHT for Column Detection under Different Conditions of Data Quality

Cited by 8 publications

References 20 publications

Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Factors affecting the implementation of automated progress monitoring of rebar using vision-based technologies

Automated rebar diameter classification using point cloud data based machine learning

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

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