Single-Shot Visual Relationship Detection for the Accurate Identification of Contact-Driven Hazards in Sustainable Digitized Construction

Author:

Kim Daeho1ORCID,Goyal Ankit2,Lee SangHyun3ORCID,Kamat Vineet R.3ORCID,Liu Meiyin4

Affiliation:

1. Civil and Mineral Engineering, University of Toronto, 35 St. George St., Toronto, ON M5S 1A4, Canada

2. NVIDIA Seattle Robotics Lab, 11431 Willows Rd., Redmond, WA 98052, USA

3. Civil and Environmental Engineering, University of Michigan, 2350 Hayward St., Ann Arbor, MI 48109, USA

4. Civil and Environmental Engineering, Rutgers University, 96 Frelinghuysen Rd., Piscataway, NJ 08854, USA

Abstract

Deploying construction robots alongside workers presents the risk of unwanted forcible contact—a critical safety concern. To address a semantic digital twin where such contact-driven hazards can be monitored accurately, the authors present a single-shot deep neural network (DNN) model that can perform proximity and relationship detections simultaneously. Given that workers and construction robots must sometimes collaborate in close proximity, their relationship must be considered, along with proximity, before concluding an event is a hazard. To address this issue, we leveraged a unique two-in-one DNN architecture called Pixel2Graph (i.e., object + relationship detections). The potential of this DNN architecture for relationship detection was confirmed by follow-up testing using real-site images, achieving 90.63% recall@5 when object bounding boxes and classes were given. When integrated with existing proximity monitoring methods, single-shot visual relationship detection will enable the accurate identification of contact-driven hazards in a digital twin platform, an essential step in realizing sustainable and safe collaboration between workers and robots.

Funder

National Science Foundation

Natural Sciences and Engineering Research Council

Publisher

MDPI AG

Reference31 articles.

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3. McKinsey&Company (2023, March 29). Rise of the Platform Era: The Next Chapter in Construction Technology. Available online: https://www.mckinsey.com/.

4. AMR (Allied Market Research) (2023, March 29). Construction Robotics Market Statistics. Available online: https://www.alliedmarketresearch.com/.

5. (2023, March 29). BLS, Bureau of Labor Statistics, Census of Fatal Occupational Injuries (CFOI), Available online: www.bls.gov/iif/oshcfoi1.html.

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