检索结果-内蒙古大学图书馆

road segmentation with image-LiDAR data fusion in deep neural network

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第47-48期79卷 35503-35518页

作者： Liu, Huafeng Yao, Yazhou Sun, Zeren Li, Xiangrui Jia, Ke Tang, Zhenming Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Chengdu Univ Informat Technol Sch Comp Sci Chengdu Peoples R China

Robust road segmentation is a key challenge in self-driving research. Though many image based methods have been studied and high performances in dataset evaluations have been reported, developing robust and reliable road segmentation is still a major challenge. Data fusion across different sensors to improve the performance of road segmentation is widely considered an important and irreplaceable solution. In this paper, we propose a novel structure to fuse image and LiDAR point cloud in an end-to-end semantic segmentation network, in which the fusion is performed at decoder stage instead of at, more commonly, encoder stage. During fusion, we improve the multi-scale LiDAR map generation to increase the precision of multi-scale LiDAR map by introducing pyramid projection method. Additionally, we adapted the multi-path refinement network with our fusion strategy and improve the road prediction compared with transpose convolution with skip layers. Our approach has been tested on KITTI road dataset and have a competitive performance.

关键词： road segmentation Data fusion Deep learning

来源：评论

学校读者我要写书评

暂无评论

CSANet: Cross-Scale Axial Attention Network for road segmentation

引用

REMOTE SENSING 2023年第1期15卷 3-3页

作者： Cao, Xianghai Zhang, Kai Jiao, Licheng Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China

road segmentation from remote sensing images is an important task in many applications. However, due to the high density of roads and the complex background, the roads are often occluded by trees. This makes accurate road segmentation a challenge task. Most existing road segmentation networks rely on convolutions with small kernels;however, these methods often cannot obtain satisfying results because the long-range dependencies are not captured and the intrinsic relationships between feature maps at different scales are not fully exploited. In this paper, a deep neural network based on a cross-scale axial attention mechanism is proposed to address this problem. This model enables low-resolution features to aggregate global contextual information from high-resolution features. Among them, the axial attention mechanism realizes global attention by using vertical and horizontal attention sequentially. With this strategy, the dense long-range dependencies can be captured with extremely low computational cost. The cross-scale mechanism enables the model to effectively combine the high-resolution fine-grained features and the low-resolution coarse-grained features. The proposed method enables the network to propagate the information without losing details. Our method achieves IoUs of 58.98 and 65.28 on the Massachusetts roads dataset and DeepGlobe dataset and outperforms other methods.

关键词： road segmentation remote sensing information fusion cross scale

来源：评论

学校读者我要写书评

暂无评论

Dual geometric perception for cross-domain road segmentation

引用

DISPLAYS 2023年 76卷

作者： Zou, Wenbin Long, Ruijing Zhang, Yuhang Liao, Muxin Zhou, Zhi Tian, Shishun Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Inst Artificial Intelligence & Adv Commun Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China

road segmentation plays an important role in navigation systems and autonomous driving. However, many methods in road segmentation are based on supervised learning and suffer from performance degradation in the real world. There is a certain domain gap (distribution shift problem) between the source domain (training data) and the target domain (testing data). In this paper, we propose a Dual-Geometric Perception (DGP) approach for cross-domain road segmentation, which jointly uses semantic and dual-geometric information to learn the domain-invariant feature for road segmentation. First, we propose an RGB-N dual stream network structure, which effectively fuses normal vector information and RGB information to reduce domain gap. Moreover, a dual geometric adversarial learning strategy is proposed to utilize depth-aware and normal vector features to perform better domain alignment. Furthermore, a self-training learning strategy is used to further improve the model's generalizability in the target domain. Extensive experiments demonstrate that our proposed DGP achieves superior performance on lane-to-lane and lane-to-sidewalk road domain adaptation tasks.

关键词： road segmentation Domain adaptation Dual geometric perception Self-training

来源：评论

学校读者我要写书评

暂无评论

An Automatic road Surface segmentation in Non-Urban Environments: A 3D Point Cloud Approach With Grid Structure and Shallow Neural Networks

引用

IEEE ACCESS 2024年 12卷 33035-33044页

作者： Dowajy, Mohammad Somogyi, Arpad Jozsef Barsi, Arpad Lovas, Tamas Budapest Univ Technol & Econ Fac Civil Engn Dept Photogrammetry & Geoinformat H-1111 Budapest Hungary

Automatic road segmentation from three-dimensional point cloud data has gained increasing interest recently. However, it is still challenging to do this task automatically due to the wide variations of roads and complex environments, especially in non-urban areas. This research proposed a comprehensive approach for using shallow neural networks to segment non-urban road point clouds to support autonomous driving applications. The proposed approach involves converting raw point cloud data into a regular grid of cells or partial clouds. Initially, a shallow neural network based on cells' properties (cell plane fitting error, cell average intensity, cell elevation range, and cell weighted density) was employed to extract road cells from raw point cloud data. The road cells were refined and segmented into inside-road and road border point clouds based on morphologic operations. A point-wise shallow neural network was used to extract road points from the border point clouds based on intensity and geometric features (roughness, curvature, and change rate of the normal). A precise road surface point cloud is obtained by merging the inside-road and filtered border point clouds. The method performance was evaluated for two datasets captured using a mobile laser scanner (MLS). In the first dataset, the road points were extracted at average completeness, correctness, quality, and overall accuracy of 98.40%, 99.13%, 97.56%, and 98.47%, respectively. Similarly, the method achieved high scores for the second dataset with 97.22% completeness, 99.02% correctness, 96.29% quality, and 98.71% overall accuracy. The method performance demonstrates an advancement when compared to various state-of-the-art methods and also confirms its adaptability to different road environments.

关键词： road segmentation 3D point cloud shallow neural network cell point cloud plan fitting weighted density geometric features

来源：评论

学校读者我要写书评

暂无评论

Wasserstein Distance-Based Domain Adaptation and Its Application to road segmentation

Wasserstein Distance-Based Domain Adaptation and Its Applica...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kono, Seita Ueda, Takaya Arriaga-Varela, Enrique Nishikawa, Ikuko Ritsumeikan Univ Kusatsu Shiga 5258577 Japan Stroly Inc Kyoto 6008258 Japan

ISBN: (纸本)9780738133669

Domain adaptation is used in applying a classifier acquired in one data domain to another data domain. A classifier obtained by supervised training with labeled data in an original source domain can also be used for classification in a target domain in which the labeled data are difficult to collect with the help of domain adaptation. The most recently proposed domain adaptation methods focus on data distribution in the feature space of a classifier and bring the data distribution of both domains closer through learning. The present work is based on an existing unsupervised domain adaptation method, in which both distributions become closer through adversarial training between a target data encoder to the feature space and a domain discriminator. We propose to use the Wasserstein distance to measure the distance between two distributions, rather than the well-known Jensen-Shannon divergence. Wasserstein distance, or earth mover's distance, measures the length of the shortest path among all possible pairs between a corresponding pair of variables in two distributions. Therefore, minimization of the distance leads to overlap of the corresponding data pair in source and target domain. Thus, the classifier trained in the source domain becomes also effective in the target domain. The proposed method usingWasserstein distance shows higher accuracies in the target domains compared with an original distance in computer experiments on semantic segmentation of map images.

关键词： Domain adaptation Wasserstein distance road segmentation

来源：评论

学校读者我要写书评

暂无评论

DDCAttNet: road segmentation Network for Remote Sensing Images 16th

DDCAttNet: Road Segmentation Network for Remote Sensing Imag...

引用

16th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Yuan, Genji Li, Jianbo Lv, Zhiqiang Li, Yinong Xu, Zhihao Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Inst Ubiquitous Networks & Urban Comp Qingdao 266070 Peoples R China

ISBN: (纸本)9783030861308;9783030861292

Semantic segmentation of remote sensing images based on deep convolutional neural networks has proven its effectiveness. However, due to the complexity of remote sensing images, deep convolutional neural networks have difficulties in segmenting objects with weak appearance coherences even though they can represent local features of object effectively. The road networks segmentation of remote sensing images faces two major problems: high inter-individual similarity and ubiquitous occlusion. In order to address these issues, this paper develops a novel method to extract roads from complex remote sensing images. We designed a Dual Dense Connected Attention network (DDCAttNet) that establishes long-range dependencies between road features. The architecture of the network is designed to incorporate both spatial attention and channel attention information into semantic segmentation for accurate road segmentation. Experimental results on the benchmark dataset demonstrate the superiority of our proposed approach both in quantitative and qualitative evaluation.

关键词： Remote sensing road segmentation Attention mechanism

来源：评论

学校读者我要写书评

暂无评论

Weakly Supervised road segmentation in High-Resolution Remote Sensing Images Using Point Annotations

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Lian, Renbao Huang, Liqin Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350008 Peoples R China Internet Of Things Key Lab Informat Collect & Pro Digital Fujian Fuzhou 350108 Peoples R China Fujian Jiangxia Univ Coll Elect & Informat Sci Fuzhou 350108 Peoples R China

road segmentation methods based on deep neural networks have achieved great success in recent years, but creating accurate pixel-wise training labels is still a boring and expensive task, especially for large-scale high-resolution remote sensing images (HRSIs). Inspired by the stacked hourglass model for human joints detection, we propose a weakly supervised road segmentation method using point annotations in this article. First, we design a patch-based deep convolutional neural network (DCNN) model for road seeds and background points detection and train the model using point annotations. Then, in the process of road segmentation, the DCNN model detects a series of road and background points that are used to train a Support Vector Machine Classifier (SVC) for classifying each pixel into road or nonroad. According to the local geometry of road and the inaccurate classification of SVC, a multiscale and multidirection Gabor filter (MMGF) is put forward to estimate the road potential. Finally, the active contour model based on local binary fitting energy (LBF-Snake) is introduced to extract the road regions from the inhomogeneous road potential. Qualitative and quantitative comparisons show that our method achieves results close to the fully supervised semantic methods without considering the annotation cost and outperforms them given a fixed budget.

关键词： roads Image segmentation Annotations Semantics Training Remote sensing Estimation Active contour model Gabor filter point annotation remote sensing images road segmentation weakly supervised segmentation

来源：评论

学校读者我要写书评

暂无评论

Implementation of road segmentation Using U-Net Model on Single Board Computer 22

Implementation of Road Segmentation Using U-Net Model on Sin...

引用

Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications

作者： Esa Prakasa Dary Zhafran Dwi Astharini Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Indonesia Al Azhar University of Indonesia Indonesia Universitas Al Azhar Indonesia Indonesia

ISBN: (纸本)9781450397902

Technological developments in the era of globalization, several companies are competing in the field of artificial intelligence by developing autonomous drive systems. Training and road segmentation testing in this study were carried out using deep learning with the U-Net architecture method. The advantage of this method over other methods is that U-Net retains the full context of the input image. The road segmentation algorithm is implemented using Python programming. The algorithm is then executed on a single board computer. The parameters sought are the first accuracy with a value of 91.65 %, the second precision with a value of 75.53 %, the third recall with a value of 93.19 %, the fourth F1-Score with a value of 82.99 %, and the last IOU with a value of 71.36 %. The live segmentation algorithm can still detect roads, but some scenes involving objects are not included in the road segmentation process. This is due to several factors such as confusing light conditions, blur, shadows, and colors. Live segmentation using RGB frame produces an average FPS value of 0.30, and without RGB produces an average FPS value of 1.82.

关键词： single board computer road segmentation U-Net architecture

来源：评论

学校读者我要写书评

暂无评论

A road segmentation Method Based on Reorganized LiDAR Points and Line Scanning 13

A Road Segmentation Method Based on Reorganized LiDAR Points...

引用

13th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Zhihui Gu, Hao Yin, Ning Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

road segmentation is an important part of autonomous driving vehicles. Reliable road segmentation results are a prerequisite for autonomous driving tasks, e.g., path planning In this paper, we propose a road segmentation method based on 3D point cloud organization and line scanning of LiDAR data. Our model assumes that road areas are always flatter than non-road areas. First, we propose an adjacent-line-difference (ALD) feature to define the flatness of the point cloud. Using this feature, the approximate road area can be estimated. Then, we proposed a horizontal and vertical scanning strategy to obtain a more accurate road area. In order to prove the effectiveness of our method, experiments are conducted on the public KITTI-road benchmark. The experimental results prove that the proposed approach can achieve one of the best performances among all LiDAR-based methods.

关键词： road segmentation LiDAR Adjacent-line-difference Line scanning

来源：评论

学校读者我要写书评

暂无评论

Stagewise Unsupervised Domain Adaptation With Adversarial Self-Training for road segmentation of Remote-Sensing Images

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Zhang, Lefei Lan, Meng Zhang, Jing Tao, Dacheng Wuhan Univ Sch Comp Sci Inst Artificial Intelligence Wuhan 430072 Peoples R China Univ Sydney Fac Engn Sch Comp Sci Sydney NSW 2006 Australia JD Explore Acad Beijing 102600 Peoples R China

road segmentation from remote-sensing images is a challenging task with wide ranges of application potentials. Deep neural networks have advanced this field by leveraging the power of large-scale labeled data, which, however, are extremely expensive and time-consuming to acquire. One solution is to use cheap available data to train a model and deploy it to directly process the data from a specific application domain. Nevertheless, the well-known domain shift (DS) issue prevents the trained model from generalizing well on the target domain. In this article, we propose a novel stagewise domain adaptation model called roadDA to address the DS issue in this field. In the first stage, roadDA adapts the target domain features to align with the source ones via generative adversarial networks (GANs)-based interdomain adaptation. Specifically, a feature pyramid fusion module is devised to avoid information loss of long and thin roads and learn discriminative and robust features. Besides, to address the intradomain discrepancy in the target domain, in the second stage, we propose an adversarial self-training method. We generate the pseudo labels of the target domain using the trained generator and divide it to labeled easy split and unlabeled hard split based on the road confidence scores. The features of hard split are adapted to align with the easy ones using adversarial learning and the intradomain adaptation process is repeated to progressively improve the segmentation performance. Experiment results on two benchmarks demonstrate that roadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods. The code is available at https://***/LANMNG/roadDA.

关键词： roads Image segmentation Adaptation models Task analysis Data models Feature extraction Predictive models Remote sensing (RS) road segmentation self-training unsupervised domain adaptation (UDA)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：