Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162909
Title: Anomaly detection for industrial parts using PatchCore
Authors: Kuah, Zheng Xuan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Kuah, Z. X. (2022). Anomaly detection for industrial parts using PatchCore. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162909
Abstract: The 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.
URI: https://hdl.handle.net/10356/162909
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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