Road Segmentation
31 papers with code • 3 benchmarks • 5 datasets
Road Segmentation is a pixel wise binary classification in order to extract underlying road network. Various Heuristic and data driven models are proposed. Continuity and robustness still remains one of the major challenges in the area.
Benchmarks
These leaderboards are used to track progress in Road Segmentation
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Datasets
DeepGlobe
Massachusetts Roads Dataset
Pothole Mix
Polarimetric Imaging for Perception
ChesapeakeRSC
Most implemented papers
RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation
In order to reach real-time process speed, a light-weight, high-throughput CNN architecture namely RoadNet-RT is proposed for road segmentation in this paper.
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
pshams55/ASPN
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We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects.
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
wrld/PRoGAN
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Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-View Transformation
JonDoe-297/cross-view
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CVPR 2021
Furthermore, our model runs at 35 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
lanmng/roadda
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In this paper, we propose a novel stagewise domain adaptation model called RoadDA to address the DS issue in this field.
SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image which is essential to extract road connectivity.
Fast Road Segmentation via Uncertainty-aware Symmetric Network
morancyc/usnet
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The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road.
Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts
prassanna-ravishankar/slither
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We propose a method to detect and segment roads with a random forest classifier of local experts with superpixel based machine-learned features.
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization
Given the logit scores produced by the base segmentation model, each pixel is given a pseudo-label that is obtained by optimally thresholding the logit scores in each image patch.
Semi-Supervised Confidence-Level-based Contrastive Discrimination for Class-Imbalanced Semantic Segmentation
KangchengLiu/Crack-Detection-and-Segmentation-Dataset-for-UAV-Inspection
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First and foremost, to make the model operate in a semi-supervised manner, we proposed the confidence-level-based contrastive learning to achieve instance discrimination in an explicit manner, and make the low-confidence low-quality features align with the high-confidence counterparts.
