Image generation of hazardous situations in construction sites using text-to-image generative model for training deep neural networks

Kim, Hayoung; Yi, June-Seong

doi:10.1016/j.autcon.2024.105615

Automation in Construction

2024

DOI: 10.1016/j.autcon.2024.105615

|View full text |Cite

Image generation of hazardous situations in construction sites using text-to-image generative model for training deep neural networks

Hayoung Kim,

June-Seong Yi

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Massive-Scale construction dataset synthesis through Stable Diffusion for Machine learning training

Hong,

Choi,

Ham

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

Massive-Scale construction dataset synthesis through Stable Diffusion for Machine learning training

Hong,

Choi,

Ham

et al. 2024

Advanced Engineering Informatics

View full text Add to dashboard Cite

Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport

Cao-Feijóo,

Pérez-Canosa,

Pérez-Castelo

et al. 2024

JMSE

View full text Add to dashboard Cite

Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Image generation of hazardous situations in construction sites using text-to-image generative model for training deep neural networks

Cited by 2 publications

References 31 publications

Massive-Scale construction dataset synthesis through Stable Diffusion for Machine learning training

Massive-Scale construction dataset synthesis through Stable Diffusion for Machine learning training

Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport

Contact Info

Product

Resources

About