Deep image generative modeling and statistical testing for industrial anomaly detection
This thesis addresses the challenge of anomaly detection in images, for industrial applications. It explores advanced methodologies employing both classical image processing techniques and modern generative modeling approaches, specifically focusing on Normalizing Flows and Diffusion Models. As anom...
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| Format: | doctoralThesis |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/20.500.12008/49470 |
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| _version_ | 1868889936937091072 |
|---|---|
| author | Tailanián, Matías |
| author_browse | Tailanián, Matías |
| author_facet | Tailanián, Matías |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Tailanián Matías, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Tailanián, Matías |
| dc.date.none.fl_str_mv | 2024 2025-04-02T15:00:19Z 2025-04-02T15:00:19Z |
| dc.format.none.fl_str_mv | 206 p. application/pdf |
| dc.identifier.none.fl_str_mv | Tailanián, M. Deep image generative modeling and statistical testing for industrial anomaly detection [en línea]. Tesis de doctorado. Montevideo : Udelar. FI. IIE, 2024. 1688-2784 https://hdl.handle.net/20.500.12008/49470 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Udelar.FI |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Anomaly Anomaly detection Industrial anomaly detection Image generative modeling Diffusion models Likelihood estimation A contrario NFA Number of false alarms Image processing AI Artificial intelligence Machine learning Anomalías Detección de anomalías Detección de anomalías industriales Modelado de imágenes generativo Modelos de difusión Estimación de verosimilitud NFA Número de falsas alarmas Procesamiento de imágenes IA Inteligencia artificial Aprendizaje automático |
| dc.title.none.fl_str_mv | Deep image generative modeling and statistical testing for industrial anomaly detection |
| dc.type.none.fl_str_mv | Tesis de doctorado info:eu-repo/semantics/doctoralThesis info:eu-repo/semantics/acceptedVersion |
| description | This thesis addresses the challenge of anomaly detection in images, for industrial applications. It explores advanced methodologies employing both classical image processing techniques and modern generative modeling approaches, specifically focusing on Normalizing Flows and Diffusion Models. As anomalies are rare by definition, collecting normal samples is generally easier and more feasible in industrial settings than acquiring comprehensive datasets with labeled anomalies. Therefore, the focus of this research is on unsupervised methods, and one-class methods, where the idea is to model the “normality” and detect anomalies as everything that deviates from this model. Initially, a multi-scale anomaly detection method based on classical image processing techniques is proposed, leveraging an a contrario approach to control the number of false alarms. Subsequently, a novel method called U-Flow is introduced, which employs a U-shaped architecture in Normalizing Flows to achieve anomaly detection with automatic thresholding. Then, this thesis further explores the use of Diffusion Models for anomaly detection, presenting the Diffusion Anomaly Detection (DAD) method. This work incorporates scorebased generative models and inpainting techniques to refine anomaly detection capabilities. Additionally, a new diffusion-based method called RIFA (Random Inpainting For Anomaly detection) is proposed as a completely unsupervised alternative. Finally, the techniques and knowledge gained from Diffusion Models are applied to a completely different application: counter-forensics. Throughout the whole thesis, a special emphasis is placed on bridging the gap between theoretical research and practical industrial applications, setting the theoretical foundations for obtaining automatic segmentations of anomalies, by performing statistical tests and controlling the number of false alarms using the a contrario framework. Experimental results on standard datasets validate the effectiveness of the proposed methods, highlighting substantial performance gains in some cases. The final chapter applies the best-performing method to two industrial problems : quality control in manufacturing leather samples for the upholstery industry, and defect detection in fruits, demonstrating its practical viability and impact on improving quality control processes in these industries In addition, this research contributes to the open-source community with several code repositories and has resulted in four published papers so far, and hopefully, more will follow. Future work will particularly focus on improving likelihood estimation with Diffusion Models and expanding its applicability to other industrial domains. |
| eu_rights_str_mv | openAccess |
| format | doctoralThesis |
| id | anni_06495e28ca7d541a7877ccc736ff3d00 |
| identifier_str_mv | Tailanián, M. Deep image generative modeling and statistical testing for industrial anomaly detection [en línea]. Tesis de doctorado. Montevideo : Udelar. FI. IIE, 2024. 1688-2784 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/49470 |
| publishDate | 2024 |
| publishDateSort | 2024 |
| publisher.none.fl_str_mv | Udelar.FI |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Deep image generative modeling and statistical testing for industrial anomaly detectionTailanián, MatíasAnomalyAnomaly detectionIndustrial anomaly detectionImage generative modelingDiffusion modelsLikelihood estimationA contrarioNFANumber of false alarmsImage processingAIArtificial intelligenceMachine learningAnomalíasDetección de anomalíasDetección de anomalías industrialesModelado de imágenes generativoModelos de difusiónEstimación de verosimilitudNFANúmero de falsas alarmasProcesamiento de imágenesIAInteligencia artificialAprendizaje automáticoThis thesis addresses the challenge of anomaly detection in images, for industrial applications. It explores advanced methodologies employing both classical image processing techniques and modern generative modeling approaches, specifically focusing on Normalizing Flows and Diffusion Models. As anomalies are rare by definition, collecting normal samples is generally easier and more feasible in industrial settings than acquiring comprehensive datasets with labeled anomalies. Therefore, the focus of this research is on unsupervised methods, and one-class methods, where the idea is to model the “normality” and detect anomalies as everything that deviates from this model. Initially, a multi-scale anomaly detection method based on classical image processing techniques is proposed, leveraging an a contrario approach to control the number of false alarms. Subsequently, a novel method called U-Flow is introduced, which employs a U-shaped architecture in Normalizing Flows to achieve anomaly detection with automatic thresholding. Then, this thesis further explores the use of Diffusion Models for anomaly detection, presenting the Diffusion Anomaly Detection (DAD) method. This work incorporates scorebased generative models and inpainting techniques to refine anomaly detection capabilities. Additionally, a new diffusion-based method called RIFA (Random Inpainting For Anomaly detection) is proposed as a completely unsupervised alternative. Finally, the techniques and knowledge gained from Diffusion Models are applied to a completely different application: counter-forensics. Throughout the whole thesis, a special emphasis is placed on bridging the gap between theoretical research and practical industrial applications, setting the theoretical foundations for obtaining automatic segmentations of anomalies, by performing statistical tests and controlling the number of false alarms using the a contrario framework. Experimental results on standard datasets validate the effectiveness of the proposed methods, highlighting substantial performance gains in some cases. The final chapter applies the best-performing method to two industrial problems : quality control in manufacturing leather samples for the upholstery industry, and defect detection in fruits, demonstrating its practical viability and impact on improving quality control processes in these industries In addition, this research contributes to the open-source community with several code repositories and has resulted in four published papers so far, and hopefully, more will follow. Future work will particularly focus on improving likelihood estimation with Diffusion Models and expanding its applicability to other industrial domains.Subvención de financiación parcial de la Agencia Nacional de Investigación e Innovación de Uruguay.Udelar.FITailanián Matías, Universidad de la República (Uruguay). Facultad de Ingeniería.2025-04-02T15:00:19Z2025-04-02T15:00:19Z2024Tesis de doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersion206 p.application/pdfTailanián, M. Deep image generative modeling and statistical testing for industrial anomaly detection [en línea]. Tesis de doctorado. Montevideo : Udelar. FI. IIE, 2024.1688-2784https://hdl.handle.net/20.500.12008/49470reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)oai:colibri.udelar.edu.uy:20.500.12008/494702026-04-14T10:27:47Z |
| spellingShingle | Deep image generative modeling and statistical testing for industrial anomaly detection Tailanián, Matías Anomaly Anomaly detection Industrial anomaly detection Image generative modeling Diffusion models Likelihood estimation A contrario NFA Number of false alarms Image processing AI Artificial intelligence Machine learning Anomalías Detección de anomalías Detección de anomalías industriales Modelado de imágenes generativo Modelos de difusión Estimación de verosimilitud NFA Número de falsas alarmas Procesamiento de imágenes IA Inteligencia artificial Aprendizaje automático |
| status_str | acceptedVersion |
| title | Deep image generative modeling and statistical testing for industrial anomaly detection |
| title_full | Deep image generative modeling and statistical testing for industrial anomaly detection |
| title_fullStr | Deep image generative modeling and statistical testing for industrial anomaly detection |
| title_full_unstemmed | Deep image generative modeling and statistical testing for industrial anomaly detection |
| title_short | Deep image generative modeling and statistical testing for industrial anomaly detection |
| title_sort | Deep image generative modeling and statistical testing for industrial anomaly detection |
| topic | Anomaly Anomaly detection Industrial anomaly detection Image generative modeling Diffusion models Likelihood estimation A contrario NFA Number of false alarms Image processing AI Artificial intelligence Machine learning Anomalías Detección de anomalías Detección de anomalías industriales Modelado de imágenes generativo Modelos de difusión Estimación de verosimilitud NFA Número de falsas alarmas Procesamiento de imágenes IA Inteligencia artificial Aprendizaje automático |
| url | https://hdl.handle.net/20.500.12008/49470 |