open access

https://hdl.handle.net/2115/95313

Snow- or ice-covered road detection in winter road surface conditions using deep neural networks

英語

2024-06-03

John Wiley & Sons

Computer-aided civil and infrastructure engineering

39(19)

2935-2950

Traffic accidents occur frequently in cold and snow- or ice-covered regions due to weather changes that occur during the winter season. To detect the snow- or ice-covered roads in road surface conditions, road surface images captured using fixed-point cameras installed along the route are sufficient. This paper proposes a snow- or ice-covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end-to-end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow- or ice-covered road areas without requiring pixel-level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM-based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real-world road surface images demonstrate the effectiveness of the proposed method.

学術雑誌論文

Accepted Manuscript

This is the peer reviewed version of the following article: Moroto, Y., Maeda, K., Ogawa, T., & Haseyama, M. (2024). Snow- or ice-covered road detection in winter road surface conditions using deep neural networks. Computer-Aided Civil and Infrastructure Engineering, 1–16., which has been published in final form at https://doi.org/10.1111/mice.13233. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

1093-9687