Spyros Theodoropoulos, Dimitrios Dardanis, Georgios Makridis, Patrik Zajec, Jože M. Rožanec, Dimosthenis Kyriazis, Panayiotis Tsanakas
{"title":"通过 StyleGAN 潜在空间导航增强对新型视觉缺陷的稳健性:制造业用例","authors":"Spyros Theodoropoulos, Dimitrios Dardanis, Georgios Makridis, Patrik Zajec, Jože M. Rožanec, Dimosthenis Kyriazis, Panayiotis Tsanakas","doi":"10.1007/s10845-024-02415-1","DOIUrl":null,"url":null,"abstract":"<p>Visual Quality Inspection is an integral part of the manufacturing process that is becoming increasingly automated with the advent of Industry 4.0. While very beneficial, AI-driven Computer Vision Algorithms and Deep Neural Networks face several issues that may impede their adoption in practical real-life settings such as a manufacturing shop floor. One such issue arising during an AI classifier’s continuous operation is the frequent lack of robustness to novel defects appearing for the first time. Such unanticipated inputs can pose a significant risk to cyber-physical applications as a resulting out-of-context decision could compromise the integrity of the production process. While recent Machine Learning methods can theoretically tackle this problem from different angles (e.g., open-set recognition, semi-supervised learning, intelligent data augmentation), applying them to a real-life setting with a small, imbalanced dataset and high inter-class similarity can be challenging. This paper confronts such a use case aiming at the automation of the visual quality inspection of shaver shell brand prints from the electronics industry and characterized by data scarcity and the existence of small local defects. To that end, we introduce a novel data augmentation approach based on the latent space manipulation of StyleGAN, where defect data is intentionally synthesized to simulate novel inputs that can help form a boundary of the model’s knowledge. Our approach shows promising results compared to well-established open-set recognition and semi-supervised methods applied to the same problem, while its consistent performance across classifier embeddings indicates lower coupling to the final classifier.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"33 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing robustness to novel visual defects through StyleGAN latent space navigation: a manufacturing use case\",\"authors\":\"Spyros Theodoropoulos, Dimitrios Dardanis, Georgios Makridis, Patrik Zajec, Jože M. 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While recent Machine Learning methods can theoretically tackle this problem from different angles (e.g., open-set recognition, semi-supervised learning, intelligent data augmentation), applying them to a real-life setting with a small, imbalanced dataset and high inter-class similarity can be challenging. This paper confronts such a use case aiming at the automation of the visual quality inspection of shaver shell brand prints from the electronics industry and characterized by data scarcity and the existence of small local defects. To that end, we introduce a novel data augmentation approach based on the latent space manipulation of StyleGAN, where defect data is intentionally synthesized to simulate novel inputs that can help form a boundary of the model’s knowledge. 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Enhancing robustness to novel visual defects through StyleGAN latent space navigation: a manufacturing use case
Visual Quality Inspection is an integral part of the manufacturing process that is becoming increasingly automated with the advent of Industry 4.0. While very beneficial, AI-driven Computer Vision Algorithms and Deep Neural Networks face several issues that may impede their adoption in practical real-life settings such as a manufacturing shop floor. One such issue arising during an AI classifier’s continuous operation is the frequent lack of robustness to novel defects appearing for the first time. Such unanticipated inputs can pose a significant risk to cyber-physical applications as a resulting out-of-context decision could compromise the integrity of the production process. While recent Machine Learning methods can theoretically tackle this problem from different angles (e.g., open-set recognition, semi-supervised learning, intelligent data augmentation), applying them to a real-life setting with a small, imbalanced dataset and high inter-class similarity can be challenging. This paper confronts such a use case aiming at the automation of the visual quality inspection of shaver shell brand prints from the electronics industry and characterized by data scarcity and the existence of small local defects. To that end, we introduce a novel data augmentation approach based on the latent space manipulation of StyleGAN, where defect data is intentionally synthesized to simulate novel inputs that can help form a boundary of the model’s knowledge. Our approach shows promising results compared to well-established open-set recognition and semi-supervised methods applied to the same problem, while its consistent performance across classifier embeddings indicates lower coupling to the final classifier.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.