Segmentation of Vehicles and Roads by a Low-Channel Lidar
| Autor: | Jae-Seol Lee, Jun-Hyeong Jo, Tae-Hyoung Park |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Channel (digital image) business.industry Computer science Mechanical Engineering 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Convolutional neural network GeneralLiterature_MISCELLANEOUS Computer Science Applications Convolution Lidar Transformation (function) 0502 economics and business Automotive Engineering Low density Effective method Segmentation Computer vision Artificial intelligence business |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 20:4251-4256 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | An effective method to segment vehicles and roads is proposed for autonomous vehicles using low-channel 3D lidar. The distance-view transformation is newly proposed to overcome the low density of top-view data of lidar. In addition, a dilated convolution structure is proposed to expand the receptive field of a convolutional neural network. The proposed network improves the accuracy of segmentation. The experimental results are presented to verify the usefulness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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