Efficient deep models for monocular road segmentation
By Gabriel L. Oliveira, Wolfram Burgard, Thomas Brox. 2016
217 citations recorded by the source corpus.
Subfields: Automotive Engineering, Civil and Structural Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Lei Tai
- Mauro Bellone
- Lennart Svensson
- Mattias Wahde
- Luca Caltagirone
- Daniela Rus
- Nicholas A. Roy
- Gabriel L. Oliveira
In Civil and Structural Engineering
In Computer Vision and Pattern Recognition
- Lei Tai
- Wolfram Burgard
- Luc Van Gool
- Daniela Rus
- Mohammadreza Zolfaghari
- Abhinav Valada
- Lennart Svensson
- Thomas Brox
Citations in the corpus
- Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection (2017, methodology): “For this work, we make use of Fast-Net [27], a network for human body part segmentation, which will provide our action recognition network with body pose information.”
- Safe Visual Navigation via Deep Learning and Novelty Detection (2017, background): “[24] provide more recent examples of deep learning used to segment image pixels into drivable routes, which can then be used for planning.”
- Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps (2018, background): “Vision-based approaches to road segmentation not based on road markings include both model-based [20] and learning-based [21] methods and can achieve good performance in ideal conditions, but fail under poor illumination.”
- Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “In [122], a new CNN structure was proposed with the aim to achieve a good trade-off between segmentation quality and runtime.”
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “Oliveira et al. (2016) used the same strategy for the road segmentation and lane prediction applied on the autonomous vehicles.”
- LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks (2018, methodology): “The learning rate η was decayed using the poly learning policy [28] implemented as:”
- AdapNet: Adaptive semantic segmentation in adverse environmental conditions (2017, methodology): “[17] proposed improvements that reduce the number of parameters and additional refinement stages that improve the resolution of segmentation.”