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dc.contributor.authorKuah, Zheng Xuanen_US
dc.identifier.citationKuah, Z. X. (2022). Anomaly detection for industrial parts using PatchCore. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThe ability to detect imperfect parts is essential for components in a large-scale industrial manufacturing. The decision of an anomaly detection revolves around a binary problem. This paper will delve into a state-of-the-art method of anomaly detection known as PatchCore and its effectiveness on various datasets. Several datasets are considered along with specific domain area such as the Magnetic tiles. By extending the usage of a memory bank for pixel level patch features from an auto encoder, PatchCore can achieve high level accuracy pixel-level anomaly detection score of up to 99.6%. Looking beyond traditional computing, the model will be considered for edge computing on Internet of Things for faster inference speed.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleAnomaly detection for industrial parts using PatchCoreen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorYeo Chai Kiaten_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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