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Multi-feature View-based Shallow Convolutional Neural Network for Road Segmentation
2020
IEEE Access
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The multi-feature views are fed to a fully-connected neural network to accurately segment the road regions. ...
To overcome these issues, a Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) is proposed that utilizes the abstract features extracted from the explicitly derived representations ...
The proposed Multi-feature View-based Shallow Convolutional Neural Network (MVS-CNN) utilizes gradients information as additional features along with the input image. ...
doi:10.1109/access.2020.2968965
fatcat:wnkpqzbsbnddhpv2u7mhapcsuq
Road Network Extraction Using Atrous Spatial Pyramid Pooling
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). ...
The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. ...
Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). ...
doi:10.35940/ijitee.h74590.78919
fatcat:wu4fzvkxljde5htike7pkczc34
DUAL PYRAMIDS ENCODER-DECODER NETWORK FOR SEMANTIC SEGMENTATION IN GROUND AND AERIAL VIEW IMAGES
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Without post-processing and multi-scale testing, the proposed network has achieved state-of-the-art performances on two challenging benchmark image datasets for both ground and aerial view scenes. ...
In this work, we proposed a dual pyramids encoder-decoder deep neural network (DPEDNet) to tackle the above issues. ...
As shown in Fig.1 (a) , our proposed deep neural network consists of two pyramids: multi-resolution feature aggregation pyramid for the encoder and multi-scale dense atrous convolution pyramid for the ...
doi:10.5194/isprs-archives-xliii-b2-2020-605-2020
fatcat:nnd6sfay7baf3ou57avm3w7oe4
City-Wide Traffic Flow Forecasting Using a Deep Convolutional Neural Network
2020
Sensors
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For modelling the spatial correlations of the traffic flows between current and adjacent road segments, we employ a multi-layer fully convolutional framework to perform cross-correlation calculation and ...
In this paper, we propose a deep-learning-based multi-branch model called TFFNet (Traffic Flow Forecasting Network) to forecast the short-term traffic status (flow) throughout a city. ...
Thus, we build a model with many layers based on ResNet, which satisfies the actual demand for extracting the hierarchical spatial dependencies of road segments. ...
doi:10.3390/s20020421
pmid:31940830
pmcid:PMC7014408
fatcat:soqft3ihkfbkxekdymdedqboom
Multi-scale Feature Extraction and Fusion Net: Research on UAVs Image Semantic Segmentation Technology
2023
Journal of ICT Standardization
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Aiming at the above problems, this paper presents a semantic segmentation method for UAV images, which introduces a multi-scale feature extraction and fusion module based on the encoding-decoding framework ...
By combining multi-scale channel feature extraction and multi-scale spatial feature extraction, the network can focus more on certain feature layers and spatial regions when extracting features. ...
Figure 5 shows the predicted segmentation performance of each network model for buildings and roads. ...
doi:10.13052/jicts2245-800x.1115
fatcat:cc3liblwqvbftihu3ch43bgwcm
A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
2022
Applied Sciences
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In recent years, convolutional neural-network-based crack segmentation methods have performed excellently. ...
To address these problems, an encoder–decoder crack segmentation network based on multi-scale contextual information enhancement is proposed. ...
[10] used a histogram-based threshold segmentation method to extract road cracks. Cheng et al. ...
doi:10.3390/app122111135
fatcat:uvwy3cmbwrde7lwgcyiyisebf4
Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet
2019
Remote Sensing
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Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. ...
Road extraction base on high-resolution remote sensing images has become a hot topic. ...
[31] proposed a Y-type convolutional neural network for road segmentation of high-resolution visible remote sensing images. ...
doi:10.3390/rs11212499
fatcat:aysxedo6uvg75mhcaglrtf4yiy
Review of pavement defect detection methods
2020
IEEE Access
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In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. ...
Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. ...
Many deep learning based methods, especially deep convolution neural networks, have been proposed for VOLUME 8, 2020 road crack detection. ...
doi:10.1109/access.2020.2966881
fatcat:bjtik5zm3rg45epe4bt5cw73ge
Dense Fusion Classmate Network for Land Cover Classification
[article]
2019
arXiv
pre-print
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Recently, FCNs based methods have made great progress in semantic segmentation. ...
In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover classification. ...
Introduction In recent years, convolution neural network (CNN) based models have achieved huge success in a wide range of tasks of computer version, such as semantic segmentation, which has a wide array ...
arXiv:1911.08169v1
fatcat:jgicg664yjcsvnvnvhvydb2n3i
ALS Point Cloud Classification by Integrating an Improved Fully Convolutional Network into Transfer Learning with Multi-Scale and Multi-View Deep Features
2020
Sensors
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Second, these feature maps are fed into the pre-trained DenseNet201 model to derive deep features, which are used as input for a fully convolutional neural network with convolutional and pooling layers ...
In this paper, we propose an ALS point cloud classification method to integrate an improved fully convolutional network into transfer learning with multi-scale and multi-view deep features. ...
Acknowledgments: The authors thank the anonymous reviewers for their helpful comments about this article. ...
doi:10.3390/s20236969
pmid:33291256
fatcat:w2hcxi3gvvgsbphvrb6bdpzrga
Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3+
2022
Sensors
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The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. ...
In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. ...
based on depth neural network. ...
doi:10.3390/s22197477
pmid:36236574
pmcid:PMC9571339
fatcat:vikg7tk5vzeelj65rz5ly2obzi
Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency
[article]
2022
arXiv
pre-print
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We propose a Heterogeneous Spatial Correlation (HSC) model to capture the spatial correlation based on a specific measurement, where the traffic data of varying road segments can be heterogeneous (i.e. ...
We propose a Multi-fold Correlation Attention Network (MCAN), which relies on the HSC model to explore multi-fold spatial correlations and leverage LSTM networks to capture multi-fold temporal correlations ...
., convolution neural network (CNN), graph convolutional network (GCN)) have been applied to traffic speed/flow prediction over road network structure for capturing spatial cor-relations [7, 21, 24, 22 ...
arXiv:2104.09083v2
fatcat:vmznptlysbh5heg7yuevfrtb6m
Survey on Semantic Segmentation using Deep Learning Techniques
2019
Neurocomputing
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Semantic segmentation is a challenging task in computer vision systems. ...
For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. ...
ACKNOWLEDGMENT The authors express their gratitude to University Technology Belfort-Montbeliard and Higher Education Commission of Pakistan for providing the support and necessary requirement for completion ...
doi:10.1016/j.neucom.2019.02.003
fatcat:aelsfl7unvdw5j2rtyqhtgqrsm
Intelligent Control System of Unmanned Vehicle Based on CAN Controller
2022
Advances in Multimedia
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Expansion convolution of different expansion rates is used to obtain multi-scale target information and to fuse the feature information at different scales during upsampling to enrich the semantic information ...
The experimental results show that the average semantic segmentation accuracy of the obstacles in concentrated vehicles, pedestrians, and bicycles reached 84.6%, and the detection and segmentation accuracy ...
Road Semantic Segmentation Based on VGG16-FCN8 Network. e output of each layer of the convolutional neural network is a three-dimensional tensor denoted by H × W × d, where H and W are spatial dimensions ...
doi:10.1155/2022/7448905
fatcat:welb7pzuzbettn6t6otezpalp4
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
[article]
2021
arXiv
pre-print
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Graph structures have shown promise as a modeling framework, with recent advances in spatio-temporal modeling via graph convolution neural networks, improving the performance or extending the prediction ...
The model is based on the powerful attention-based spatio-temporal graph convolution architecture but utilizes various channels to integrate different sources of information, explicitly builds spatio-temporal ...
Convolutional neural networks (CNN) have been employed to extract spatial features of grid-based data and handle highdimensional spatio-temporal data. ...
arXiv:2110.01535v1
fatcat:zldz52z64ffi5or25ay76svgwu
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