Efficient deep models for monocular road segmentation
| Autor: | Thomas Brox, Wolfram Burgard, Gabriel L. Oliveira |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
business.industry Segmentation-based object categorization Computer science 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation 020207 software engineering 02 engineering and technology Image segmentation Frame rate Convolutional neural network 0502 economics and business 0202 electrical engineering electronic engineering information engineering RGB color model Segmentation Computer vision Artificial intelligence business |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2016.7759717 |
| Popis: | This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between segmentation quality and runtime. This is achieved by a new mapping between classes and filters at the expansion side of the network. The network is trained end-to-end and yields precise road/lane predictions at the original input resolution in roughly 50ms. Compared to the state of the art, the network achieves top accuracies on the KITTI dataset for road and lane segmentation while providing a 20× speed-up. We demonstrate that the improved efficiency is not due to the road segmentation task. Also on segmentation datasets with larger scene complexity, the accuracy does not suffer from the large speed-up. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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