Anomaly detection with diffusion models

master's thesis

Matic Fučka (Author), Danijel Skočaj (Mentor), Vitjan Zavrtanik (Co-mentor)

Abstract

Surface defect detection is a critical challenge in ensuring product quality, as defects can pose safety risks and diminish product lifespan. Unsupervised anomaly detection is a closely related problem that tries to detect anomalies without any explicit information about them during the training phase. While deep learning has introduced numerous methods, the problem has barely seen any attempts with diffusion models. To address this, we propose Transfusion, a TRANSparent difFUSION model. Our approach focuses on iterative erasure of anomalies. To achieve this we redesigned the base diffusion process and the base architecture for diffusion models. Notably, Transfusion achieves exceptional performance in anomaly detection, surpassing state-of-the-art results on VisA dataset with an impressive 98.5% AUROC and achieving competitive results on MVTec AD with an 99.2% AUROC. This advancement offers promising prospects for reliable and efficient surface defect detection in manufacturing processes.

Keywords

computer vision;anomaly detection;diffusion models;computer science;master's thesis;

Data

Language:	English
Year of publishing:	2023
Typology:	2.09 - Master's Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[M. Fučka]
UDC:	004.052.42(043.2)
COBISS:	164268803
Views:	95
Downloads:	30
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary title:	Detekcija anomalij z difuzijskimi modeli
Secondary abstract:	Detekcija površinskih napak je ključni izziv pri zagotavljanju kakovosti izdelkov, saj lahko napake predstavljajo varnostno tveganje in skrajšajo življenjsko dobo izdelka. Nenadzorovana detekcija anomalij je tesno povezan problem, ki poskuša detektirati anomalije brez predhodnih informacij o njih med procesom učenja. Predlagane so bile številne metode, ki temeljijo na globokem učenju, vendar zelo malo z difuzijskimi modeli. Da bi to rešili, predlagamo model TRANSparent difFUSION ali na kratko Transfusion. Naš pristop se osredotoča na iterativno brisanje anomalij. Da bi to dosegli, smo preoblikovali osnovni difuzijski proces in osnovno arhitekturo za difuzijske modele. Transfusion dosega izjemne rezultate pri detekciji anomalij, saj z impresivno 98,5-odstotno vrednostjo mere AUROC presega vse dosedanje rezultate na podatkovni množici VisA in dosega vrhunske rezultate na podatkovni množici MVTec AD z 99,2-odstotno vrednostjo mere AUROC. Ta napredek ponuja obetavne možnosti za zanesljivo in učinkovito detekcijo površinskih napak v proizvodnih procesih.
Secondary keywords:	detekcija anomalij;difuzijski modeli;magisteriji;Odkrivanje anomalij (varnost računalniških sistemov);Računalniški vid;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS):	Master's thesis/paper
Study programme:	1000471
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	XXVIII, 79 str.
ID:	19888174