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contributor authorWei Guan
contributor authorShuai Wang
contributor authorZeren Chen
contributor authorGuohua Wu
contributor authorYi Fang
contributor authorHaoyan Zhang
contributor authorGuoqiang Wang
date accessioned2024-04-27T22:43:02Z
date available2024-04-27T22:43:02Z
date issued2024/01/01
identifier other10.1061-JCCEE5.CPENG-5436.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297325
description abstractThe estimation of material volume in a construction vehicle’s bucket is a crucial prerequisite for automation, as well as for productivity assessment and efficient material transport. Although some studies have been conducted in this field, the accuracy and speed of inference have been suboptimal, and specific implementation strategies have not been proposed. To address these issues, this paper proposes a new approach. The proposed approach has three main components. First, a novel image preprocessing framework based on three-dimensional (3D) grayscale terrain is presented. Second, a semantic mask-level data set is constructed to facilitate future research in this area. Third, a combined neural network and probabilistic approach is proposed to estimate the material volume, with speed and accuracy as metrics. Transfer learning is introduced to improve training efficiency and accuracy. The proposed material volume estimation method is implemented on three different devices, addressing the problem from the development phase to the application phase. The advantages and disadvantages of each device are discussed in depth. The results demonstrate that the proposed approach achieves an impressive average accuracy of 98.20% on all three devices, with real-time or semi–real-time volume estimation feasible on each. In summary, this paper proposes a new approach to estimate the material volume in a construction vehicle’s bucket, addressing issues of accuracy and speed of inference and providing specific implementation strategies. The results demonstrate the effectiveness of the proposed approach, which has potential applications in automation and productivity assessment in the construction industry.
publisherASCE
titleA Deep Learning and Vision-Based Solution for Material Volume Estimation Considering Devices’ Applications
typeJournal Article
journal volume38
journal issue1
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/JCCEE5.CPENG-5436
journal fristpage04023039-1
journal lastpage04023039-19
page19
treeJournal of Computing in Civil Engineering:;2024:;Volume ( 038 ):;issue: 001
contenttypeFulltext


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