Using Hue, Saturation, and Value Color Space for Hydraulic Excavator Idle Time Analysis

Zou, Junhao; Kim, Hyoungkwan

doi:10.1061/(asce)0887-3801(2007)21:4(238)

Cited by 136 publications

(41 citation statements)

References 14 publications

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“…Motion information thus plays a critical role in evaluating ongoing works performed by equipment and workers, generally with existing safety and health rules (e.g., checklists). As a means to collect motion information on a jobsite, action recognition techniques that capture particular actions in sequential images have been studied in construction [24][25][26][27][28][29].…”

Section: Potential Roles Of Computer Vision-based Approachesmentioning

confidence: 99%

Computer vision techniques for construction safety and health monitoring

Seo

Han

Lee

et al. 2015

Advanced Engineering Informatics

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422

145

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Section: Potential Roles Of Computer Vision-based Approachesmentioning

confidence: 99%

Computer vision techniques for construction safety and health monitoring

Seo

Han

Lee

et al. 2015

Advanced Engineering Informatics

Self Cite

422

145

View full text Add to dashboard Cite

“…Given its advantages of low cost and minimal infrastructure, 2D image or video based techniques have been applied in construction for various operation-level applications including construction progress monitoring [5], construction entity detection and tracking [15], and productivity and safety analyses [16] [17]. Various techniques have been explored to recognize objects (e.g., equipment, workers) on construction sites from 2D image or video, such as hue, saturation, and value (HSV) color space [18], background subtraction [19], histogram of color (HOC) [20], and template matching [21].…”

Section: D Image and Video-based Methodsmentioning

confidence: 99%

Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

Chen¹,

Fang²,

Cho³

2016

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Recognizing construction assets from as-is point cloud data of construction environment provides essential information for engineering and management applications including progress monitoring, safety management, supply-chain management, and quality control. This study proposes a fast and automated processing pipeline for construction target assets recognition from scattered as-is point clouds. The recognition tasks can be subdivided into object detection, which involves computing the bounding box around each construction equipment, and object classification, which involves labelling point cloud clusters from discrete equipment categories, such as backhoe loader, bulldozer, dump truck, excavator and front loader. The object detection step consists of point cloud down-sampling, segmentation and clustering. For the object classification step, machine learning methods were employed to determine class membership probability using features derived from the ESF (Ensemble of Shape Functions) descriptor. The classifiers were trained on synthetic point clouds generated from CAD (Computer-aided Design) models. The method was validated using laser scanned point clouds from an equipment yard. The test results demonstrate promising advancements towards semantic labelling and scene understanding of point cloud data.

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“…Zou and Kim (2007) proposed using Hue, Saturation, and Value (HSV) color space to track the equipment. Detecting the workers through the color of their hardhats was studied by Weerasinghe and Ruwanpura (2009).…”

Section: Cv-based Methods In Constructionmentioning

confidence: 99%

Evaluating the Performance of Convolutional Neural Network for Classifying Equipment on Construction Sites

Soltani¹,

Karandish²,

Ahmed³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Estimating the productivity of construction operations is one of the most challenging tasks for project managers. Therefore, the construction industry always looks toward new advancements for automating this process. New automated methods for productivity estimation aim to detect the types, locations, and activities of construction equipment based on sensory data. Computer Vision (CV) is one of the most promising automated methods and it provides an affordable opportunity for estimating the productivity since it only requires regular surveillance cameras for data collection, which are available on many construction sites. One of the widely used CV methods for classifying equipment is Histogram of Oriented Gradient (HOG). Additionally, Bag of Words (BoWs) and Local Binary Pattern (LBP) are other types of descriptors widely used for the object classification. However, these methods reduce the dimensions of the image features to train the classifiers for object detection, which may reduce the reliability of the results. Convolutional Neural Networks (CNN), which are a special type of Artificial Neural Networks (ANN) with deeper layer structure, provide a better approach for object detection compared to the conventional methods due to their deeper understanding of the object features. Furthermore, the advancements in Graphical Processing Units (GPU) made this computationally heavy method more applicable in practice. This paper aims to evaluate the performance of CNN for detecting equipment on construction sites. Several configurations of CNN are trained for detecting multiple equipment (i.e. dump trucks, excavators and loaders). The results of these configurations are compared with those of conventional detectors.

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Using Hue, Saturation, and Value Color Space for Hydraulic Excavator Idle Time Analysis

Cited by 136 publications

References 14 publications

Computer vision techniques for construction safety and health monitoring

Computer vision techniques for construction safety and health monitoring

Automated Equipment Recognition and Classification from Scattered Point Clouds for Construction Management

Evaluating the Performance of Convolutional Neural Network for Classifying Equipment on Construction Sites

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