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road model prediction based unstructured road detection

Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第11期14卷 822-834页

作者： Wen-hui ZUO Tuo-zhong YAO Department of Information Science and Electronic Engineering Zhejiang University Ningbo Institute of Materials Technology&Engineering Chinese Academy of Sciences

Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile *** outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture *** region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and *** this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the *** main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road *** scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the ***,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more *** results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.

关键词： road detection Surface layout road model prediction Temporal smoothing

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Automation in road distress detection,diagnosis and treatment

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Journal of road Engineering 2024年第1期4卷 1-26页

作者： Xu Yang Jianqi Zhang Wenbo Liu Jiayu Jing Hao Zheng Wei Xu School of Future Transportation Chang'an UniversityXi'an 710064China School of Highway Chang'an UniversityXi'an 710064China School of Information Engineering Chang'an UniversityXi'an 710064China

road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic *** the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the *** study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt ***,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are *** addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack *** their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time ***,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and *** paper addresses the challenges of precise defect detection,condition assessment,and unmanned *** the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

关键词： road detection road diagnosis road treatment Deep learning Intelligent maintenance

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Robust Free-Space detection in Urban roads based on MSER Extraction Using Gradient Images

Robust Free-Space Detection in Urban Roads based on MSER Ext...

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第37届中国控制会议

作者： Le Xin Juan Song Yangzhou Chen Jiangbi Hu College of Metropolitan Transportation Beijing University of Technology Beijing Key Laboratory of Traffic Engineering College of Automation Beijing Union University College of Architecture and Civil Engineering Beijing University of Technology

In this paper, a robust algorithm to detect free-spaces in urban roads using video sequences captured on-board is proposed, mainly aiming at the complex urban road conditions such as the change of road structure and dense traffic conditions, as well as the changing illumination. This algorithm is based on MSER（Maximally Stable Extremal Region） extraction using gradient images, which includes the following two aspects. Firstly, the gradient images are calculated by the Sobel operator formed as the spatial filter template. MSERs are extracted from these gradient images with an improved MSER extraction algorithm with linear complexity. Then some shape analysis is applied on each MSER after shape representation is computed by the contour extraction. On the basis of selecting the proper pixel location of the initial seed point, the region of the road area is determined where the initial seed point falls, based on the assumption that the region directly in front of the host vehicle is just the road area. Experimental results show that this method can run well in various traffic scenarios, and achieve real-time and robust road detection results.

关键词： road detection On-board Video Processing Free-Space detection MSER Gradient Images

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Application of UAV in road Safety in Intelligent Areas

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Journal of Geographical Research 2019年第4期2卷 15-21页

作者： Yanan Xu Jianxin Qin Pengcheng He Zhuan Chen Hunan Key Laboratory of Geospatial Big Data Mining and Application Hunan Normal UniversityChangsha410081 China School of Surveying and Mapping Engineering East China University of TechnologyNanchang330013China

With the continuous development of remote sensing(RS)technology,the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV).The application of UAV low altitude RS technology in road safety in intelligent area has certain practical *** can provide safety warning for most drivers,and provide auxiliary decision-making for the road supervision *** the collection,processing,calculation and analysis of the road image,the UAV can find out the road obstacles with potential safety hazards,identify the road pit,calculate the radius and depth of the road pit through the digital mapping system,predict the accident risk according to different speed and provide scientific basis for the road safety *** the same time,UAV can provide repair scheme for damaged roads,estimate the quantity of materials needed for repair,and achieve the target of resource saving and efficiency *** experimental results show that the UAV can not only provide scientific prediction information for driving safety,but also provide relatively accurate material consumption for road repair.

关键词： UAV Low-altitude RS technology road safety road repair road detection

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Local Map Construction Based on 3D-LiDAR and Camera

Local Map Construction Based on 3D-LiDAR and Camera

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第三十九届中国控制会议

作者： Hui Qin Jing Li Junzheng Wang Qingbin Wu Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

The local map can update the local environment information in real time, which provides the environment information for the local dynamic planning of the robot. In this paper, a local cost map construction method based on 3D-LIDAR and camera is proposed. We use camera to detect lane lines in structured road and 3D-LIDAR to detect road boundaries in unstructured environment, and then use DS evidence reasoning to determine the current local road information. Dynamic obstacle information in the environment is obtained through 3D point cloud data segmentation, which is fused with the road information to get 3D point cloud information in the local range and generate local cost map according to it. Experiments show that the method in this paper can accurately extract the current road information whether in structured roads or unstructured roads. The fused local cost map can enable the robot to perform reasonable local planning and complete navigation on the current road.

关键词： Local map Multi-sensor fusion road detection DS evidence reasoning

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road Segmentation Based on Learning Classification

Road Segmentation Based on Learning Classification

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Yiming Nie Zhenping Sun Daxue Liu Tao Wu Bin Dai College of Mechatronical Engineering and Automation National University of Defense Technology

In vision navigation tasks, the road segmentation is a useful method. Usually, roads can be detected using image segmentation and related image process methods. However such methods always rely on specific road prior knowledge, and they are difficult to be realized in different environments. In this paper the learning classification is proposed. In the learning process the roads of different environments are labeled and learned. In the classification process the road is segmented and selected. Experiments results show that even the roads change the correct results could be got for online process.

关键词： Monocular vision image segment super pixel road detection

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