Automating daily inspection for Expressways using anomaly detection model



Published Sep 4, 2023
Yuta Shirakawa Satoshi Ito Reiko Noda Naoto Yoshitani Masahide Wake Honoka Takano


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.

Abstract 146 | PDF Downloads 160



Anomaly detection, Visual inspection, Machine learning

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H. (2018), Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone. Computer Aided Civil and Infrastructure Engineering, Vol. 33, Issue 12, pp 1127-1141, 2018.

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

Lin, T., Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie S. (2017), Feature Pyramid Networks for Object Detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR),

Ito, S. (2022), Multi-class road damage detection using multiple instance learning [Translated from Japanese.], Vision Engineering Workshop 2022
Regular Session Papers