Pseudo normal image generation for anomaly detection on road surface

Naoyuki Mori; Noriko Takemura; Yasushi Yagi

doi:10.1117/12.2522245

16 July 2019 Pseudo normal image generation for anomaly detection on road surface

Naoyuki Mori, Noriko Takemura, Yasushi Yagi

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720B (2019) https://doi.org/10.1117/12.2522245
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

Citation Download Citation

Naoyuki Mori, Noriko Takemura, and Yasushi Yagi "Pseudo normal image generation for anomaly detection on road surface", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720B (16 July 2019); https://doi.org/10.1117/12.2522245

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