Data-driven scene parsing method for recognizing construction site objects in the whole image

Kim, Hongjo; Kim, Kinam; Kim, Hyoungkwan

doi:10.1016/j.autcon.2016.08.018

Cited by 54 publications

(24 citation statements)

References 76 publications

(105 reference statements)

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“…">Supervised LearningAn alternative to the scan vs. BIM method is to use a library of preclassified object attributes/features as templates for semantic feature extraction. For instance, a library of preclassified images were used as training data for a supervised learning sequence to find walls and construction materials in images taken from construction sites [23,24]. In point cloud processing, the preclassified object attributes can be generated through different means, such as the planned or as-built BIM [25,26], Monte Carlo simulation to generate synthetic point clouds of objects subject to random instrumental measurement errors [22], or manual classification of structural elements and its attributes from previously acquired point clouds [27].…”

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confidence: 99%

“…However, point clouds acquired from construction sites contain outliers due to dust, occlusions, and moving objects, which requires additional robust outlier removal procedures [22]. Other group of studies that focus on semantic labeling of point clouds acquired from construction sites mainly require either an up-to-date 4D BIM [15][16][17][18][19][20][21][22]25] or a library of historical preclassified objects [23,24,[26][27][28]31], which may be neither available nor practical. In addition, to provide a generalizable solution, a point cloud processing framework is required whose effectiveness is independent of subjectively predefined thresholds [22].…”

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confidence: 99%

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.

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Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

2019

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“…Construction sites evolve over time and involve a large number of entities (Teizer 2015). Kim et al (2016) proposed a novel scheme to recognize construction objects in the entire image using a data-driven scene parsing method. The system was nonparametric and scalable to the number of recognizable objects.…”

Section: Related Workmentioning

confidence: 99%

“…An average pixel-wise recognition rate of 81.48% was achieved for real construction site images. Inspired by Kim et al (2016) and Kim and Caldas (2013), we propose a system using context information obtained by data-driven scene parsing to enhance action recognition of construction workers.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing action recognition of construction workers using data-driven scene parsing

Yang

2018

Journal of Civil Engineering and Management

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Vision-based action recognition of construction workers has attracted increasing attention for its diverse applications. Though state-of-the-art performances have been achieved using spatial-temporal features in previous studies, considerable challenges remain in the context of cluttered and dynamic construction sites. Considering that workers actions are closely related to various construction entities, this paper proposes a novel system on enhancing action recognition using semantic information. A data-driven scene parsing method, named label transfer, is adopted to recognize construction entities in the entire scene. A probabilistic model of actions with context is established. Worker actions are first classified using dense trajectories, and then improved by construction object recognition. The experimental results on a comprehensive dataset show that the proposed system outperforms the baseline algorithm by 10.5%. The paper provides a new solution to integrate semantic information globally, other than conventional object detection, which can only depict local context. The proposed system is especially suitable for construction sites, where semantic information is rich from local objects to global surroundings. As compared to other methods using object detection to integrate context information, it is easy to implement, requiring no tedious training or parameter tuning, and is scalable to the number of recognizable objects.

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3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions

Wei

Kasireddy

Akinci

2018

Advanced Computing Strategies for Engineering

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Data-driven scene parsing method for recognizing construction site objects in the whole image

Cited by 54 publications

References 76 publications

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction

Enhancing action recognition of construction workers using data-driven scene parsing

3D Imaging in Construction and Infrastructure Management: Technological Assessment and Future Research Directions

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