Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Language: search.filters.language.enSubject: search.filters.subject.Augmented Reality

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  • Article
    Using Augmented Reality (AR) in Vocational Education Programs to Teach Occupational Health and Safety (OHS)
    (The Design and Technology Association, 2021) Hülagü, R.; Erkarslan, Ö.
    The aim of this research is to design a system that will raise awareness among vocational education students about occupational health and safety and the integration of Augmented Reality (AR) systems into the application/concept. Simply, projected on the work force surface, the AR system warns the students as they perform actions that pose a risk, need caution and may result in accidents. Therefore, by repetitive warnings, students learn the faultiness of actions in a faster pace and develop and insightful awareness. The research involves a literature review and two experiments studies in Çınarlı Vocational and Technical High School (CVHS) with high school and Dokuz Eylül University Mechanical Engineering (DEU ME) students. A system is designed according to the findings from these studies. As a result, students learnt to be more cautious, and the number of mistakes they make decreased. This will result in decrease in the number of occupational accidents, deaths and financial loss. The project presents an innovative method applicable both to the industry and the training a qualified work force. © 2021, The Design and Technology Association. All rights reserved.
  • Article
    Citation - Scopus: 20
    Estrus Detection and Dairy Cow Identification With Cascade Deep Learning for Augmented Reality-Ready Livestock Farming
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arıkan, İ.; Ayav, T.; Seçkin, A.Ç.; Soygazi, F.
    Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming. © 2023 by the authors.