DOAJ Open Access 2023

Anomaly detection from images in pipes using GAN

Shigeki Yumoto Takumi Kitsukawa Alessandro Moro Sarthak Pathak Taro Nakamura +1 lainnya

Abstrak

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.

Topik & Kata Kunci

Technology Mechanical engineering and machinery Control engineering systems. Automatic machinery (General) Machine design and drawing Technology (General) Industrial engineering. Management engineering Automation Information technology

Penulis (6)

Shigeki Yumoto

Takumi Kitsukawa

Alessandro Moro

Sarthak Pathak

Taro Nakamura

Kazunori Umeda

Format Sitasi

APA MLA BibTeX

Yumoto, S., Kitsukawa, T., Moro, A., Pathak, S., Nakamura, T., Umeda, K. (2023). Anomaly detection from images in pipes using GAN. https://doi.org/10.1186/s40648-023-00246-y

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1186/s40648-023-00246-y
Akses: Open Access ✓