Video dataset for the detection of safe and unsafe behaviours in workplaces.

Computer vision Deep learning Occupational health and safety Safe and unsafe behaviours Workplace

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Oct 2024
Historique:
received: 11 06 2024
revised: 18 07 2024
accepted: 29 07 2024
medline: 3 9 2024
pubmed: 3 9 2024
entrez: 3 9 2024
Statut: epublish

Résumé

Real-time detection of safe and unsafe behaviours in production facilities is very important to prevent these behaviours before they occur. In this context, this study presents a high-resolution video-based dataset of safe and unsafe behaviours obtained from a closed production facility for use in occupational accident prevention. The dataset was collected from the security cameras of a production facility operating in an organised industrial zone in Eskişehir, Turkey, in November and December 2022, after obtaining the necessary permissions from company officials and employees. A total of 8 behaviour classes, 4 classes of safe and 4 classes of unsafe behaviours, were identified for the dataset and 691 video clips containing these behaviours were obtained. The video clips created for the dataset are in MP4 format at 1920×1080 pixels and 24 frames per second. In the dataset, the safe behaviour classes are Safe Walkway, Authorized Intervention, Closed Panel Cover and Safe Carrying, while the unsafe behaviour classes are Safe Walkway Violation, Unauthorized Intervention, Opened Panel Cover and Carrying Overload with Forklift.

Identifiants

pubmed: 39224505
doi: 10.1016/j.dib.2024.110791
pii: S2352-3409(24)00756-X
pmc: PMC11367630
doi:

Types de publication

Journal Article

Langues

eng

Pagination

110791

Informations de copyright

© 2024 The Author(s).

Auteurs

Oğuzhan Önal (O)

Department of Electronic and Automation, Vocational School, Bilecik Seyh Edebali University, Bilecik, Türkiye.

Emre Dandıl (E)

Department of Computer Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, Bilecik, Türkiye.

Classifications MeSH