Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6492
DC FieldValueLanguage
dc.contributor.authorXin Lien_US
dc.contributor.authorNorriza Hussinen_US
dc.contributor.authorIsmail, N. A.en_US
dc.date.accessioned2024-10-01T08:40:45Z-
dc.date.available2024-10-01T08:40:45Z-
dc.date.issued2024-
dc.identifier.issn0277-786X-
dc.descriptionScopusen_US
dc.description.abstractEnterprises are often concerned about disasters, particularly accidents involving personnel. Therefore, there is ongoing research dedicated to safeguarding the safety of employees within enterprises. In recent years, with the continuous development of machine vision technology, industry and academia have been working to adopt machine vision methods to address safety hazards in workers' production. Machine vision applications are still being researched for specific sectors and lack generalization. Algorithms commonly face issues of high computational complexity and demanding hardware requirements. This paper adopts the lightweight YOLOV5 as the baseline algorithm and enhances its accuracy using a receptive field attention mechanism. SeNet is introduced to improve the generalization of object detection, and IDetect Head is employed to increase the efficiency of the detection head. Ultimately, the algorithm's accuracy is enhanced by 3.7%, and mAP50 is increased by 3.0%. This algorithm can be deployed to Internet of Things (IoT) machine vision terminals, reducing deployment costs and improving monitoring efficiency.en_US
dc.publisherSPIEen_US
dc.subjectIDetect Headen_US
dc.subjectMachine Visionen_US
dc.subjectPersonal Safetyen_US
dc.subjectYOLOV5en_US
dc.titleDetection of construction worker safety protection equipment based on fast YOLOen_US
dc.typeInternationalen_US
dc.relation.conferenceProceedings of SPIE - The International Society for Optical Engineeringen_US
dc.identifier.doi10.1117/12.3039641-
dc.volume13259en_US
dc.relation.seminar2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024en_US
dc.description.articleno1325944en_US
dc.date.seminarstartdate2024-06-07-
dc.date.seminarenddate2024-06-09-
dc.description.placeofseminarYinchuanen_US
dc.description.typeIndexed Proceedingsen_US
item.openairetypeInternational-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptUniversiti Malaysia Kelantan-
Appears in Collections:Faculty of Data Science and Computing - Proceedings
Files in This Item:
File Description SizeFormat
Detection of construction worker safety protection equipment based on fast YOLO.pdf1.4 MBAdobe PDFView/Open
Show simple item record

Google ScholarTM

Check

Altmetric

Altmetric


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