Academic Journal
Anomaly detection from images in pipes using GAN
| العنوان: | Anomaly detection from images in pipes using GAN |
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| المؤلفون: | Shigeki Yumoto, Takumi Kitsukawa, Alessandro Moro, Sarthak Pathak, Taro Nakamura, Kazunori Umeda |
| المصدر: | ROBOMECH Journal, Vol 10, Iss 1, Pp 1-12 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Technology LCC:Mechanical engineering and machinery LCC:Machine design and drawing LCC:Technology (General) LCC:Industrial engineering. Management engineering LCC:Automation LCC:Information technology |
| مصطلحات موضوعية: | Infrastructure inspection, Sewer pipe, Deep learning, GAN, Anomaly detection, Technology, Mechanical engineering and machinery, TJ1-1570, Control engineering systems. Automatic machinery (General), TJ212-225, Machine design and drawing, TJ227-240, Technology (General), T1-995, Industrial engineering. Management engineering, T55.4-60.8, Automation, T59.5, Information technology, T58.5-58.64 |
| الوصف: | Abstract In recent years, the number of pipes that have exceeded their service life has increased. For this reason, earthworm-type robots equipped with cameras have been developed to perform regularly inspections of sewer pipes. However, inspection methods have not yet been established. This paper proposes a method for anomaly detection from images in pipes using Generative Adversarial Network (GAN). A model that combines f-AnoGAN and Lightweight GAN is used to detect anomalies by taking the difference between input images and generated images. Since the GANs are only trained with non-defective images, they are able to convert an image containing defects into one without them. Subtraction images is used to estimate the location of anomalies. Experiments were conducted using actual images of cast iron pipes to confirm the effectiveness of the proposed method. It was also validated using sewer-ml, a public dataset. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2197-4225 |
| Relation: | https://doaj.org/toc/2197-4225 |
| DOI: | 10.1186/s40648-023-00246-y |
| URL الوصول: | https://doaj.org/article/3937e45133354615a8b8d6a656a5d1cd |
| رقم الانضمام: | edsdoj.3937e45133354615a8b8d6a656a5d1cd |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21974225 |
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| DOI: | 10.1186/s40648-023-00246-y |