Vision-Based Detection of Unsafe Worker Guardrail Climbing Based on Posture and Instance Segmentation Data Fusion-Reference-Cited by-同舟云学术

Vision-Based Detection of Unsafe Worker Guardrail Climbing Based on Posture and Instance Segmentation Data Fusion

Published:2024-11 Issue:11 Volume:150 Page:
ISSN:0733-9364
Container-title:Journal of Construction Engineering and Management
language:en
Short-container-title:J. Constr. Eng. Manage.

Author:

Mei Xinyu¹,Ma Wendi¹,Xu Feng²^ORCID,Zhang Zhipeng³

Affiliation:

1. Undergraduate Research Assistant, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan Rd., Shanghai 200240, PR China.

2. Associate Professor, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan Rd., Shanghai 200240, PR China (corresponding author). ORCID: .

3. Associate Professor, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai Jiao Tong Univ., 800 Dongchuan Rd., Shanghai 200240, PR China.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCEMD4.COENG-14266

Reference63 articles.

1. Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system

2. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

3. Evaluation of guardrail systems for preventing falls through roof and floor holes

4. Bolya D. Z. Chong X. Fanyi and Y. J. Lee. 2019. “YOLACT++: Better real-time instance segmentation.” Preprint submitted December 3 2019. https://arxiv.org/abs/1912.06218.

5. Cascade R-CNN: High Quality Object Detection and Instance Segmentation