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Deep representation learning for road detection using Siamese network
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DOI：10.1007/s11042-018-6986-1.png)
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摘要
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Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI ROAD demonstrate the effectiveness of our proposed approach.
Keyword:
Road detection
Siamese network
Data fusion
Deep learning
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IF:
3
论文数:
2.0W
被引数:
3.2W
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Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks基于生成对抗网络的半监督和弱监督道路检测
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