Automating daily inspection for Expressways using anomaly detection model

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Published Sep 4, 2023
DOI https://doi.org/10.36001/phmap.2023.v4i1.3597
Yuta Shirakawa
Media AI Laboratory, Corporate Research & Development Center, Toshiba Corporation
Satoshi Ito
Media AI Laboratory, Corporate Research & Development Center, Toshiba Corporation
Reiko Noda
Media AI Laboratory, Corporate Research & Development Center, Toshiba Corporation
Naoto Yoshitani
Maintenance Strategy Headquarters, Central Nippon Expressway Company Limited
Masahide Wake
Social Infrastructure System Solutions Engineering Dept. ICT Solutions Div. Toshiba Digital Solutions Corporation
Honoka Takano
Social Infrastructure System Solutions Engineering Dept. ICT Solutions Div. Toshiba Digital Solutions Corporation

Abstract

Because of the high speed at which vehicles travel on highways, even small irregularities on the road surface can lead to serious accidents. It is important to conduct daily visual inspections to detect these abnormalities at an early stage and to repair them quickly. We are considering replacing a part of visual inspection with automatic classification using image recognition. Automating inspections will make it possible to increase inspection frequency and expected to reduce the variation in quality due to the skill of inspectors. In this paper, we report on an AI-based inspection system and evaluation results using actual highway driving video captured by an in-vehicle camera.

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Keywords

Anomaly detection, Visual inspection, Machine learning

References
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Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2021), RDD2020: An annotated image dataset for automatic road damage detection using deep learning, Data in brief, Vol. 36, pp. 107133, 2021. doi:10.1016/j.dib.2021.107133

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Ito, S. (2022), Multi-class road damage detection using multiple instance learning [Translated from Japanese.], Vision Engineering Workshop 2022
Issue
Vol. 4 No. 1 (2023): Proceedings of the Asia Pacific Conference of the PHM Society 2023
Section
Regular Session Papers
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