Improved detection network model based on YOLOv5 for warning safety in construction sites-Reference-Cited by-同舟云学术

Improved detection network model based on YOLOv5 for warning safety in construction sites

Published:2023-02-09 Issue: Volume: Page:1-11
ISSN:1562-3599
Container-title:International Journal of Construction Management
language:en
Short-container-title:International Journal of Construction Management

Author:

Ngoc-Thoan Nguyen¹,Bui Dao-Quang Thanh²³,Tran Cuong N. N.⁴,Tran Duc-Hoc²³

Affiliation:

1. Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering (HUCE), Ha Noi, Viet Nam

2. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam

3. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Ho Chi Minh City, Vietnam

4. Faculty of International Business and Economics, University of Economics and Business, Vietnam National University, Hanoi, Vietnam

Funder

Vietnam National University HoChiMinh City

Publisher

Informa UK Limited

Subject

Management of Technology and Innovation,Strategy and Management,Building and Construction

Link

https://www.tandfonline.com/doi/pdf/10.1080/15623599.2023.2171836

Reference50 articles.

1. Alexey AB. 2016. Yolo-mark gui for marking bounded boxes of objects in images for training yolo. GitHub, Online. https://github.com/AlexeyAB/Yolo_mark.

2. A bibliometric review of the status and emerging research trends in construction safety management technologies

3. Review of construction safety performance measurement methods and practices: a science mapping approach

4. Human detection based on deep learning YOLO-v2 for real-time UAV applications

5. Bureau of Labor Statistics. 2020. Number and rate of fatal work injuries, by industry. [accessed 2022 July]. https://www.bls.gov/charts/census-of-fatal-occupational-injuries/number-and-rate-of-fatal-work-injuries-by-industry.htm.

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