Road condition estimation using deep learning with hyperspectral images: detection of water and snow
| Autor: | Daniil Valme, Javier Galindos, Dhanushka Chamara Liyanage |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings of the Estonian Academy of Sciences, Vol 73, Iss 1, Pp 77-91 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1736-6046 1736-7530 |
| DOI: | 10.3176/proc.2024.1.09 |
| Popis: | Road surface condition monitoring is one of the most crucial tasks for vehicle perception systems. The presence of water, snow, ice, or any other substance covering the road surface directly affects the rolling resistance and controllability of the vehicle, which is directly related to the safety of the traffic participants. Many sensors, such as RGB cameras, infrared sensors, and mmWave sensors, are used to monitor and inspect road surfaces. The research aims to provide a tool to segment an input image into correct classes. The DeepHyperX toolbox was used for the rapid prototyping of deep learning (DL) classification models for hyperspectral images. The effectiveness of the developed algorithm in several case studies is presented, and it is verified that the low number of iterations is enough to detect the water, snow, and ice on the road surface. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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