Construction of Stretching-Bending Sequential Pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences

Wu, Yiguang; Wang, Meizhen; Liu, Xuejun; Wang, Ziran; Ma, Tianwu; Xie, Yujia; Li, Xiuquan; Wang, Xing

doi:10.3390/s21103427

Cited by 3 publications

(26 citation statements)

References 16 publications

(34 reference statements)

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“…Basic operational flow usually involves digging, rotating, unloading, and rotating. Three fundamental steps are generally involved in the recognition of work cycles [2][3][4]: (1) recognizing the atomic actions of earthmoving excavators; (2) associating atomic actions to recognize work cycles; and (3) counting the number of work cycles. The atomic actions described in steps (1) and (2) are the various postures presented by earthmoving excavators in work cycles according to the operation process [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Manual recognition requires construction managers to visually determine atomic action categories based on the posture of the earthmoving extractor and then associate atomic actions with one work cycle [3]. An advantage of this method is that construction managers are able to make project-related decisions, such as construction equipment scheduling and construction site planning, at any time on the construction site based on the results [4].…”

Section: Introductionmentioning

confidence: 99%

“…Fixed-carrier video monitoring is a near-ground remote sensing method that was described in recent studies [13][14][15][16][17][18][19][20][21]. This type of method uses monitoring cameras installed on fixed carriers of a certain height (usually higher than 3 m), such as towers [3,[13][14][15]20,21], poles [4,16], or buildings [19], to continuously monitor changes in the ground or environment. Compared with traditional satellite remote sensing methods, this method is continuous [14,17,19], has a high spatial resolution [13,19], and is low-cost [1,6,17,22].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, since the monitoring camera has a larger angle when taking pictures of the earthmoving excavators on the ground from a high position, it is possible to minimize or avoid the blocking of the monitoring object [12], thus reducing the negative impact of blocking on the recognition results [2]. By combining this method with computer vision techniques that more realistically represent human vision and manual reasoning processes, recognition based on fixed-carrier video monitoring is becoming a popular way to measure the work cycles of earthmoving excavators [3]. Representative methods in this field include recognition based on temporal sequences and recognition based on sequential patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The recognition based on the temporal sequences method involves the construction of a set of atomic actions in work cycles according to the temporal order in which they are recognized, where a set of temporal atomic actions is classified as a work cycle [3]. Chen [2] et al classified atomic actions in work cycles of an earthmoving excavator into "Digging", "Swinging", and "Loading", and then used three deep learning methods, Faster R-CNN, SORT, and 3D ResNet to identify atomic actions.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Wang

Liu

et al. 2021

Remote Sensing

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Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Wang

Liu

et al. 2021

Remote Sensing

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Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review

2022

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The progress monitoring (PM) of construction projects is an essential aspect of project control that enables the stakeholders to make timely decisions to ensure successful project delivery, but ongoing practices are largely manual and document-centric. However, the integration of technologically advanced tools into construction practices has shown the potential to automate construction PM (CPM) using real-time data collection, analysis, and visualization for effective and timely decision making. In this study, we assess the level of automation achieved through various methods that enable automated computer vision (CV)-based CPM. A detailed literature review is presented, discussing the complete process of CV-based CPM based on the research conducted between 2011 and 2021. The CV-based CPM process comprises four sub-processes: data acquisition, information retrieval, progress estimation, and output visualization. Most techniques encompassing these sub-processes require human intervention to perform the desired tasks, and the inter-connectivity among them is absent. We conclude that CV-based CPM research is centric on resolving technical feasibility studies using image-based processing of site data, which are still experimental and lack connectivity to its applications for construction management. This review highlighted the most efficient techniques involved in the CV-based CPM and accentuated the need for the inter-connectivity between sub-processes for an effective alternative to traditional practices.

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Towards Automated Measurement of As-Built Components Using Computer Vision

Perez

Tah

2023

Sensors

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Regular inspections during construction work ensure that the completed work aligns with the plans and specifications and that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision techniques for measuring as-built elements predominantly employ three-dimensional laser scanning or three-dimensional photogrammetry modeling to ascertain the geometric properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates, making the deployment of these techniques on dynamic construction project sites challenging. This paper proposes a pipeline for automating the measurement of as-built components using artificial intelligence and computer vision techniques. The pipeline requires a single image obtained with a stereo camera system to measure the sizes of selected objects or as-built components. The results in this work were demonstrated by measuring the sizes of concrete walls and columns. The novelty of this work is attributed to the use of a single image and a single target for developing a fully automated computer vision-based method for measuring any given object. The proposed solution is suitable for use in measuring the sizes of as-built components in built assets. It has the potential to be further developed and integrated with building information modelling applications for use on construction projects for progress monitoring.

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Construction of Stretching-Bending Sequential Pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences

Cited by 3 publications

References 16 publications

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

Automated Computer Vision-Based Construction Progress Monitoring: A Systematic Review

Towards Automated Measurement of As-Built Components Using Computer Vision

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