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A dynamic attention mechanism for road extraction from high-resolution remote sensing imagery using feature fusion

SCIENTIFIC REPORTS 2025年第1期15卷 1-22页

作者： Bai, Haoming Ren, Chao Huang, Zhenzhong Gu, Yao Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541006 Peoples R China

Accurate road information is critical for intelligent navigation and urban planning. Compared with traditional road detection methods, deep learning-based approaches have demonstrated significant advantages in road extraction from remote sensing imagery. However, challenges such as occlusion by vegetation and buildings, as well as the similarity between roads and surrounding objects, often lead to incomplete road extraction. To address these issues, we propose a novel deep learning model, RISENet, which consists of three main components: a dual-branch fusion encoder, a multi-layer dynamic spatial channel fusion attention mechanism (MCSA), and a hybrid feature dilation-aware decoder. The dual-branch encoder leverages dual convolutions and multi-head deep convolutions to extract fundamental features and capture fine-grained details. The feature fusion module integrates both global and local information, enhancing the model's ability to represent features effectively. The MCSA captures long-range dependencies within remote sensing images, improving the differentiation between roads and other objects. The dilation-aware decoder dynamically expands the receptive field, preserving global features while reducing the loss of fine details. The proposed RISENet was comprehensively evaluated on three distinct road segmentation benchmarks, demonstrating superior accuracies of 90.04%, 92.24%, and 88.18% respectively. In terms of visual quality and quantitative indicators, the method proposed in this study demonstrates excellent performance. The ablation experiments have also confirmed the effectiveness of the adopted loss function and fusion strategy. These fully indicate that RISENet performs remarkably well in road segmentation tasks across various datasets and exhibits considerable robustness.

关键词： Dual-branch fusion encoder Hybrid feature dilation-aware decoder Dynamic attention mechanism road segmentation Remote sensing

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road Descriptors for Fast Global Localization on Rural roads Using OpenStreetMap

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SENSORS 2023年第18期23卷 7915页

作者： Ninan, Stephen Rathinam, Sivakumar Texas A&M Univ College Stn TX 77843 USA

Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot's understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods.

关键词： OpenStreetMap localization particle filter road segmentation descriptors

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Deep Neural Network-based Detection of road Traffic Objects from Drone-Captured Imagery Focusing on road Regions

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 307-314页

作者： Nguyen, Hoanh Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City Vietnam

This paper presents a novel deep learning approach for the detection of traffic objects from drone-based imagery, focusing predominantly on the rapid and accurate detection of vehicles within road sections. The proposed method consists of two primary components: a road segmentation module and a vehicle detection network. The former leverages a residual unit with skip-connections to effectively extract road areas, while the latter employs a modified version of the YOLOv3 architecture, tailored for high-accuracy and high-speed vehicle detection. To address the issue of data imbalance, which is a pervasive challenge in drone images, this paper utilizes a range of data augmentation techniques to improve the robustness of the proposed model. Experimental results on the UAVDT and UAVid datasets exhibit that the proposed model attains a substantial boost in accuracy and inference speed of vehicle detection in comparison to the existing methods. These findings underscore the potential of the proposed approach for real-world traffic monitoring applications, where rapid and reliable vehicle detection is paramount.

关键词： Deep learning drone images vehicle detection road segmentation data imbalance

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Real-Time LIDAR-Based Urban road and Sidewalk Detection for Autonomous Vehicles

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SENSORS 2022年第1期22卷 194-194页

作者： Horvath, Erno Pozna, Claudiu Unger, Miklos Szechenyi Istvan Univ Vehicle Ind Res Ctr H-9026 Gyor Hungary Transylvania Univ Brasov Dept Product Design & Robot Brasov 500036 Romania

road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera-based or sensor-fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR-based feature extraction. For road and sidewalk detection, the current paper presents a real-time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.

关键词： autonomous vehicle open source LIDAR point cloud free-space detection road segmentation ground-non-ground segmentation obstacle detection autonomous vehicles self-driving

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A Remote Sensing Image segmentation Model Based on CGAN Combining Multi-scale Contextual Information 3rd

A Remote Sensing Image Segmentation Model Based on CGAN Comb...

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3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Wang, Xili Zhang, Xiaojuan Guo, Huimin Yu, Shuai Zhang, Shiru Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Xian Univ Sci & Technol Coll Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9783030606329;9783030606336

In order to extract targets in different scale under complex scene, this paper proposes a remote sensing image segmentation model based on Conditional Generative Adversarial Network (CGAN) combing multi-scale contextual information. This end-to-end model consists of a generative network and a discriminant network. ASegNet model fusing multi-scale contextual information is proposed as the generative network. In order to extract multi-scale contextual information, the multi-scale features of the end pooling feature map in the encoder are extracted using different proportion of dilated convolution. The multi-scale features are further fused with the global feature. The discriminant network is a convolution neural network for two category classification, determines whether the input is a generated result or the ground truth. After alternate adversarial training, the experimental results on a remote sensing road dataset show that the road segmentation results of the proposed model are superior to those of the comparable models in terms of target integrity and details preserving.

关键词： road segmentation Conditional Generative Adversarial Network Multi-scale context Feature fusion

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roadFormer: Pyramidal deformable vision transformers for road network extraction with remote sensing images

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2022年 113卷

作者： Jiang, Xiaoling Li, Yinyin Jiang, Tao Xie, Junhao Wu, Yilong Cai, Qianfeng Jiang, Jinhui Xu, Jiaming Zhang, Hui Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 JS Peoples R China Huaiyin Inst Technol Sch Humanities Huaian 223003 JS Peoples R China

The data-complete and detail-correct road network information serves as important evidence in numerous transportation-associated applications. Regular and rapid road network inventory updating is significantly necessary and meaningful to provide better services. Remote sensing images, due to their advantageous over-looking earth observation properties, have been widely used to assist in the road network interpretation tasks. However, it is still an open issue to accurately separate the road contents from the surrounding land covers in the remote sensing image with good connectivity and integrality because of the remarkably challengeable conditions of roads. In this regard, we develop a pyramidal deformable vision transformer architecture, termed as road -Former, to extract road networks with remote sensing images. Specifically, designed by a multi-context patch embedding scheme, a higher-quality token embedding can be obtained by adopting a multi-range, multi-view context observation strategy. Furthermore, formulated with a deformable transformer architecture, the semantic -relevant features can be focused on in a sparse global manner, which effectively promotes the feature repre-sentation quality and robustness. The proposed roadFormer is elaborately evaluated on three large-scale road network extraction datasets. Quantitative assessments show that the roadFormer achieves an overall perfor-mance of 0.8886 and 0.9407 with respect to the intersection over union (IoU) and F1-score metrics. In addition, contrastive evaluations also convince the promising potentiality and outstanding superiority of the roadFormer for interpreting the road sections of varying circumstances under diverse challenging image scenarios.

关键词： road extraction road segmentation Vision transformer Deformable self -attention Intelligent transportation system

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Vision-Based Intelligent Vehicle road Recognition and Obstacle Detection Method

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第7期34卷

作者： Yang, Fan Rao, Yutai Hubei Radio & TV Univ Software Engn Inst Wuhan Hubei Peoples R China Hubei Radio & TV Univ Deans Off Wuhan Hubei Peoples R China

With the development of the world economy and the accelerating process of urbanization, cars have brought great convenience to people's lives and activities, and have become an indispensable means of transportation. Intelligent vehicles have the important significance of reducing traffic accidents, improving transportation capacity and broad market prospects, and can lead the future development of the automotive industry, so they have received extensive attention. In the existing intelligent vehicle system, the laser radar is a well-deserved protagonist because of its excellent speed and precision. It is an indispensable part of achieving high-precision positioning, but to some extent, the price hindering its marketization is a major factor. Compared with lidar sensors, vision sensors have the advantages of fast sampling rate, light weight, low energy consumption and low price. Therefore, many domestic and foreign research institutions have listed them as the focus of research. However, the current vision-based intelligent vehicle environment sensing technology is also susceptible to factors such as illumination, climate and road type, resulting in insufficient accuracy and real-time performance of the algorithm. This paper takes the environment perception of intelligent vehicles as the research object, and conducts in-depth research on the existing problems in road recognition and obstacle detection algorithms, including road image vanishing point detection, road image segmentation problem, road scene based on binocular vision. Three-dimensional reconstruction and obstacle detection technology.

关键词： Intelligent vehicle vanishing point detection road segmentation obstacle detection trajectory tracking

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Robust Urban road Image segmentation 11

Robust Urban Road Image Segmentation

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11th World Congress on Intelligent Control and Automation

作者： Li, Junyang Jin, Lizuo Fei, Shumin Ma, Junyong Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China Sci & Technol Electroopt Control Lab Luoyang Henan Province Peoples R China

ISBN: (纸本)9781479958252

Urban road detection with on-board monocular camera in vehicle is still a difficult problem due to its complexity. Combining the superpixel scene segmentation with the classification of texture and structure information of the road scenes, this paper explores the method of detection the road from a single image. Processing the road texture information on a large scale and the structural information around the road on a small scale, and obtaining accurate information about the road edge with scene segmentation, the method is proved to be robust by experiments. Experimental data show that our approach achieved relatively good results in a variety of complex urban environment.

关键词： road Detection road segmentation Multi-Scale Sparse Coding SLIC K-SVD

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ChipNet: Real-Time LiDAR Processing for Drivable Region segmentation on an FPGA

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2019年第5期66卷 1769-1779页

作者： Lyu, Yecheng Bai, Lin Huang, Xinming Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA

This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time. For autonomous vehicles, drivable region segmentation is an essential step that sets up the static constraints for planning tasks. Traditional drivable region segmentation algorithms are mostly developed on camera data, so their performance is susceptible to the light conditions and the qualities of road markings. LiDAR sensors can obtain the 3D geometry information of the vehicle surroundings with high precision. However, it is a computational challenge to process a large amount of LiDAR data in real-time. In this paper, a CNN model is proposed and trained to perform semantic segmentation using data from the LiDAR sensor. An efficient hardware architecture is proposed and implemented on an FPGA that can process each LiDAR scan in 17.59 ms, which is much faster than the previous works. Evaluated using Ford and KITTI road detection benchmarks, the proposed solution achieves both high accuracy in performance and real-time processing in speed.

关键词： Autonomous vehicle road segmentation CNN LiDAR FPGA

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Fully automated roadside parking spot detection in real time with deep learning

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第23期33卷 e6006-e6006页

作者： Cicek, Emre Goren, Sezer Yeditepe Univ Dept Comp Engn TR-34755 Istanbul Turkey

Searching for a roadside parking spot in crowded cities is a burden. In this article, we propose a vision-based mobile cloud parking management solution, which is fully automated such that it can find roadside parking spots in any street with flowing traffic. To develop such a system, we employ both object detection and road segmentation methods. Thus, we do not need to manually label and train for every distinct street and do not mark out parking spot boundaries and surrounding road areas. In our approach, fully convolutional networks, specifically the FCN-VGG16 model and KITTI road dataset are used for road segmentation, whereas Faster Region-based Convolutional Neural Networks and Microsoft Common Objects in Context dataset for object detection. Our road Boundaries algorithm automatically identifies the road polygon excluding the roadside. The main contribution here is to differentiate the parked cars from the moving cars on the road and detect the available parking spot between the parked cars on the roadside and direct the driver to the nearest spot. On GPU, we achieved a frame rate of 1.5fps and up to 83% accuracy with flowing traffic and 92% with no traffic flow. These results promise a potential solution on a city-wide scale.

关键词： convolutional neural network faster region-based convolutional networks fully convolutional networks object detection parking spot detection road segmentation

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