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Pseudo normal image generation for anomaly detection on road surface
Author(s): Naoyuki Mori; Noriko Takemura; Yasushi Yagi
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Paper Abstract

In Japan, the age-related deterioration of many public roads, which were constructed in the 1960s, is demanding maintenance solutions. We propose a convolutional neural network (CNN)-based method to convert the original image to a pseudo normal road surface image. The converter selectively replaces only the abnormal data of an image with pixels corresponding to normal features, thereby creating an output image without abnormal parts. We aim to detect anomalies on the road surface by calculating the difference between a raw input image and the generated PNI image. Our experimental results confirm the effectiveness and usefulness of our method.

Paper Details

Date Published: 16 July 2019
PDF: 8 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720B (16 July 2019); doi: 10.1117/12.2522245
Show Author Affiliations
Naoyuki Mori, Osaka Univ. (Japan)
Noriko Takemura, Osaka Univ. Institute for Datability Science, (Japan)
Yasushi Yagi, Osaka Univ. (Japan)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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