Tracking indoor construction progress by deep-learning-based analysis of site surveillance video
محفوظ في:
| الحاوية / القاعدة: | Construction Innovation vol. 25, no. 2 (2025), p. 461-489 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
Emerald Group Publishing Limited
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract |
| الوسوم: |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3163979367 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1471-4175 | ||
| 022 | |a 1477-0857 | ||
| 022 | |a 0968-0365 | ||
| 024 | 7 | |a 10.1108/CI-10-2022-0275 |2 doi | |
| 035 | |a 3163979367 | ||
| 045 | 2 | |b d20250401 |b d20250630 | |
| 084 | |a 66696 |2 nlm | ||
| 100 | 1 | |a Johnny Kwok Wai Wong |u School of Built Environment, Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia | |
| 245 | 1 | |a Tracking indoor construction progress by deep-learning-based analysis of site surveillance video | |
| 260 | |b Emerald Group Publishing Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a PurposeAccurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.Design/methodology/approachA framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.FindingsThe proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.Originality/valueThe proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities. | |
| 653 | |a Accuracy | ||
| 653 | |a Construction materials | ||
| 653 | |a Estimates | ||
| 653 | |a Stacks | ||
| 653 | |a Workflow | ||
| 653 | |a Automation | ||
| 653 | |a Tracking | ||
| 653 | |a Light levels | ||
| 653 | |a Error detection | ||
| 653 | |a Construction industry | ||
| 653 | |a Construction sites | ||
| 653 | |a Radio frequency | ||
| 653 | |a Waste materials | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Hough transformation | ||
| 653 | |a Algorithms | ||
| 653 | |a Building materials | ||
| 653 | |a Object recognition | ||
| 653 | |a Deep learning | ||
| 653 | |a Counting | ||
| 653 | |a Construction | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Bameri, Fateme |u Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran | |
| 700 | 1 | |a Alireza Ahmadian Fard Fini |u School of Built Environment, Faculty of Design, Architecture and Building, University of Technology Sydney, Sydney, Australia | |
| 700 | 1 | |a Maghrebi, Mojtaba |u School of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran | |
| 773 | 0 | |t Construction Innovation |g vol. 25, no. 2 (2025), p. 461-489 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3163979367/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |