Please use this identifier to cite or link to this item: https://elib.vku.udn.vn/handle/123456789/4043
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBui, Huy Trinh-
dc.contributor.authorPhan, Le Viet Hung-
dc.contributor.authorLe, Kim Hoang Trung-
dc.contributor.authorNguyen, Huu Nhat Minh-
dc.date.accessioned2024-07-31T04:07:37Z-
dc.date.available2024-07-31T04:07:37Z-
dc.date.issued2024-07-
dc.identifier.isbn978-604-80-9774-5-
dc.identifier.urihttps://elib.vku.udn.vn/handle/123456789/4043-
dc.descriptionProceedings of the 13th International Conference on Information Technology and Its Applications (CITA 2024); pp: 319-328vi_VN
dc.description.abstractElectrical insulators are important parts of electrical systems that often have some popular issues and require maintenance such as broken and dirty. Automatic detection using computer vision models for these problems could help to enhance the safety and continuity of the electrical operation in a proactive and timely manner with lower operational and maintenance costs. In this paper, we develop a multi-task model adopting Efficient-net as our base architecture following three branches for simultaneously three learning tasks such as identifying the type, cleanness, and broken status of the insulators. To train this multi-task model, we cleaned the dataset and performed the data augmentation of the practical dataset comprising 1500 images of electrical insulators provided by CPC Vietnam collected from pole-mounted surveillance cameras and drone survey flights. Throughout the extensive evaluation, the proposed muti-task models outperformed the single-task model by around 15-20% and demonstrated a robust design for identifying multiple problems of electrical equipment.vi_VN
dc.language.isoenvi_VN
dc.publisherVietnam-Korea University of Information and Communication Technologyvi_VN
dc.relation.ispartofseriesCITA;-
dc.subjectMulti-task learningvi_VN
dc.subjectElectrical insulator inspectionvi_VN
dc.subjectEfficient-netvi_VN
dc.titleIdentify Problems of Electrical Insulators using Multi-Task Learningvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:CITA 2024 (Proceeding - Vol 2)

Files in This Item:

 Sign in to read



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.