Real-Time Cat Detection System using MobileNet-SSD V2

Alfarizi, Salman; Salsabila, Zahra; Bahrullah, Sania; Susanti, Hesty; Cahyadi, Willy Anugrah

doi:10.1109/ica58538.2023.10273134

2023 8th International Conference on Instrumentation, Control, and Automation (ICA) 2023

DOI: 10.1109/ica58538.2023.10273134

|View full text |Cite

Real-Time Cat Detection System using MobileNet-SSD V2

Salman Alfarizi,

Zahra Salsabila,

Sania Bahrullah

et al.

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...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Sanjeewani,

Neuber,

Fitzgerald

et al. 2024

Electronics

View full text Add to dashboard Cite

Despite advancements in technology, safety equipment, and training within the construction industry over recent decades, the prevalence of fatal and nonfatal injuries and accidents remains a significant concern among construction workers. Hard hats and safety vests are crucial safety gear known to mitigate severe head trauma and other injuries. However, adherence to safety protocols, including the use of such gear, is often inadequate, posing potential risks to workers. Moreover, current manual safety monitoring systems are laborious and time-consuming. To address these challenges and enhance workplace safety, there is a pressing need to automate safety monitoring processes economically, with reduced processing times. This research proposes a deep learning-based pipeline for real-time identification of non-compliance with wearing hard hats and safety vests, enabling safety officers to preempt hazards and mitigate risks at construction sites. We evaluate various neural networks for edge deployment and find that the Single Shot Multibox Detector (SSD) MobileNet V2 model excels in computational efficiency, making it particularly suitable for this application-oriented task. The experiments and comparative analyses demonstrate the pipeline’s effectiveness in accurately identifying instances of non-compliance across different scenarios, underscoring its potential for improving safety outcomes.

show abstract

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Sanjeewani,

Neuber,

Fitzgerald

et al. 2024

Electronics

View full text Add to dashboard Cite

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.

Real-Time Cat Detection System using MobileNet-SSD V2

Cited by 1 publication

References 5 publications

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Real-Time Personal Protective Equipment Non-Compliance Recognition on AI Edge Cameras

Contact Info

Product

Resources

About