Extraction of Guardrails from MMS Data Using Convolutional Neural Network
| Autor: | Hiroki Matsumoto, Yuma Mori, Hiroshi Masuda |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Mechanical Engineering Extraction (chemistry) 0211 other engineering and technologies Pattern recognition 02 engineering and technology Convolutional neural network Industrial and Manufacturing Engineering 021105 building & construction 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | International Journal of Automation Technology. 15:258-267 |
| ISSN: | 1883-8022 1881-7629 |
| Popis: | Mobile mapping systems can capture point clouds and digital images of roadside objects. Such data are useful for maintenance, asset management, and 3D map creation. In this paper, we discuss methods for extracting guardrails that separate roadways and walkways. Since there are various shape patterns for guardrails in Japan, flexible methods are required for extracting them. We propose a new extraction method based on point processing and a convolutional neural network (CNN). In our method, point clouds and images are segmented into small fragments, and their features are extracted using CNNs for images and point clouds. Then, features from images and point clouds are combined and investigated using whether they are guardrails or not. Based on our experiments, our method could extract guardrails from point clouds with a high success rate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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