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computer-vision-10

Road Segmentation

3260 papers • 126 benchmarks • 313 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.

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Benchmarks

These leaderboards are used to track progress in road-segmentation-10

Trend

Dataset

Best Model

Actions

ChesapeakeRSC

ChesapeakeRSC

DeepGlobe

DeepGlobe

Massachusetts Roads Dataset

Libraries

i

Use these libraries to find road-segmentation-10 models and implementations

Datasets

DeepGlobe

Massachusetts Roads Dataset

Massachusetts Roads Dataset

Pothole Mix

Polarimetric Imaging for Perception

Polarimetric Imaging for Perception

ChesapeakeRSC

Subtasks

No subtasks available.

Most implemented papers

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

R. Urtasun, R. Cipolla, Johann Marius Zöllner, Marvin Teichmann, Michael Weber•Wed Dec 21 2016

This paper presents an approach to joint classification, detection and semantic segmentation using a unified architecture where the encoder is shared amongst the three tasks, and performs extremely well in the challenging KITTI dataset.

748

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Massachusetts Roads Dataset

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D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

Ming Wu, Lichen Zhou, Chuang Zhang•Thu May 31 2018

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.

778 0

Deep Quaternion Networks

A. Maida, Chase J. Gaudet•Tue Dec 12 2017

This work explores the benefits of generalizing one step further into the hyper-complex numbers, quaternions specifically, and provides the architecture components needed to build deep quaternion networks, and shows improved convergence compared to real-valued and complex-valued networks.

186 0

Road Segmentation using CNN and Distributed LSTM

Yecheng Lyu, Lin Bai, Xinming Huang•Thu Aug 09 2018

In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning. The existing algorithms implement gigantic convolutional neural networks (CNNs) that are computationally expensive and time consuming. In this paper, we introduced distributed LSTM, a neural network widely used in audio and video processing, to process rows and columns in images and feature maps. We then propose a new network combining the convolutional and distributed LSTM layers to solve the road segmentation problem. In the end, the network is trained and tested in KITTI road benchmark. The result shows that the combined structure enhances the feature extraction and processing but takes less processing time than pure CNN structure.

47 0

SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments

Dinesh Manocha, Rohan Chandra, D. Kothandaraman•Thu Nov 26 2020

This work presents a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog using a new algorithm for source-free domain adaptation (SFDA) using self-supervised learning.

34 0

Semantic Binary Segmentation Using Convolutional Networks without Decoders

I. Stavness, Shubhra Aich, William van der Kamp•Mon Apr 30 2018

This paper proposes an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation, which eliminates the decoder portion of traditional encoder-decoder segmentation models and reduces the amount of computation almost by half.

33 0

RoadTracer: Automatic Extraction of Road Networks from Aerial Images

Mohammad Alizadeh, F. Bastani, Songtao He, Sofiane Abbar, H. Balakrishnan, S. Chawla, S. Madden, D. DeWitt•Sat Feb 10 2018

This work proposes RoadTracer, a new method to automatically construct accurate road network maps from aerial images that uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN.

342 0

Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information

Ji Qi, Chao Tao, Hao Wang, Yuqi Tang, Zhenqi Cui•Sun Jun 30 2019

For road extraction tasks in VHR satellite imagery, a deep neural network may perform well. But a network with certain reasoning ability as human will get a more satisfying result. To this end, we focus on how to effectively model the context information of the road and propose a well-designed spatial information inference structure (SIIS) which can add into any typical semantic segmentation network. The network with SIIS called SII-Net can not only learn the local visual characteristic of the road but also the global spatial structure information (such as the continuity and trend of the road). So, it can effectively solve the challenging occlusion problem in road detection and well preserve the continuity of the extracted road. The experimental results of two datasets show that the proposed method can improve the comprehensive performance of road extraction.

115 0

Predicting Semantic Map Representations From Images Using Pyramid Occupancy Networks

R. Cipolla, Thomas Roddick•Sun Mar 29 2020

This work presents a simple, unified approach for estimating birds-eye-view maps of their environment directly from monocular images using a single end-to-end deep learning architecture.

288 0

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Road Segmentation | State-of-the-Art